Patent Publication Number: US-9411403-B2

Title: System and method for dynamic DCVS adjustment and workload scheduling in a system on a chip

Description:
DESCRIPTION OF THE RELATED ART 
     Portable computing devices (“PCDs”) are becoming necessities for people on personal and professional levels. These devices may include cellular telephones, portable digital assistants (“PDAs”), portable game consoles, palmtop computers, and other portable electronic devices. 
     From the perspective of a user, a compact form factor is a desirable aspect in a PCD. Compact form factors, however, come with inherent design challenges. For instance, in a PCD there typically is not enough space for engineers and designers to combat thermal degradation or failure of processing components through wide spacing arrangements or inclusion of passive cooling components. Consequently, processing components in a PCD often cannot be run at their maximum rated power frequencies without producing detrimental levels of thermal energy. In a PCD, thermal energy generation must be managed through the application of various thermal management techniques that may include wilting or shutting down electronics at the expense of performance. 
     Users also desire generous power supplies in their PCDs. Due to the limited form factors, however, simply including large power sources is usually not a solution for meeting a user&#39;s power supply expectations. As such, in a PCD power consumption must be managed. Power conservation schemes seeking to minimize power consumption (whether in an effort to save power or in an effort to avoid unnecessary thermal energy generation) often dictate that processing speeds be set such that a minimum quality of service (“QoS”) level is met. 
     Thermal management techniques are employed within a PCD in an effort to seek a balance between mitigating thermal energy generation and impacting the QoS provided by the PCD. Similarly, power conservation schemes are employed within a PCD in an effort to seek a balance between power consumption and QoS level. In a PCD that has heterogeneous processing components, the ramifications of balancing those tradeoffs can be difficult to manage because the various processing components within the PCD are not created equal. 
     For instance, the power frequency supplied to a processing component per a thermal mitigation technique or a power conservation scheme may not represent the most efficient point on the processor&#39;s performance curve. As another example, there may be multiple processing components in a PCD capable of processing a given block of code and, depending on the respective static and dynamic operating factors of those components, one will be more efficient at processing that block of code than another. 
     Accordingly, what is needed in the art is a method and system for setting the power frequency of a processing component in a PCD to the frequency that represents the most efficient workload processing for that processing component without undermining ongoing thermal mitigation or power conservation goals. Further, what is needed in the art is a system and method for scheduling or allocating workload in a PCD across heterogeneous processing components based on real time, or near real time, comparative analysis of the optimum frequencies in processor performance curves without undermining ongoing thermal mitigation or power conservation goals. 
     SUMMARY OF THE DISCLOSURE 
     Various embodiments of methods and systems for dynamically adjusting operating frequency settings of one or more processing components in a portable computing device (“PCD”) are disclosed. Because individual processing components in a heterogeneous, multi-processor in a PCD may exhibit different processing efficiencies depending on various static and dynamic factors associated with each processing component, dynamic DCVS adjustment and workload scheduling techniques query performance curves in real time, or near real time, to determine optimum operating frequencies for processing components so that power efficiency is optimized when processing workloads. 
     One such method involves receiving a request to adjust an operating frequency setting of a processing component to a requested frequency (“F_req”). Upon recognizing that the operating frequency for the processing component will be adjusted, readings of one or more static and/or dynamic factors (e.g., current leakage, voltage levels, operating temperature, power supply voltage margin, etc.) associated with the operation of the processing component may be taken. Using the operating readings, performance curves associated with the processing component may be queried. The performance curves comprise a representation of the relationship between power consumption and operating frequency for the processing component when operating at a given operating temperature. The performance curves are used to determine the optimal operating frequency (“F_opt”) for the processing component. When the processing component is supplied power at the F_opt frequency, the ratio of power consumed per workload processed is optimized. The F_opt is compared to the F_req and, if F_req is greater than or equal to F_opt, the operating frequency setting of the processing component is set to F_req. If, however, the F_req is less than F_opt, the operating frequency setting of the processing component is set to F_opt. Advantageously, when the operating frequency is set to F_opt in lieu of F_req, the processing time for a workload is shortened and the power consumed to process the workload is minimized per MIPS of workload. 
     Some embodiments of the systems and methods for dynamic DCVS adjustment envision that the processing component is a multi-core, heterogeneous processor that comprises a plurality of individual processing components. In such scenarios, the readings of one or more static and/or dynamic factors (e.g., current leakage, voltage levels, operating temperature, power supply voltage margin, etc.) for each of the plurality of individual processing components may be taken and the performance curves for each queried. The performance curves may then be averaged to create a single set of performance curves representative of the group of individual processors. The optimum power frequency determined from the average performance curves may then be applied across the plurality of individual processing components. 
     Further, some embodiments of the systems and methods for dynamic DCVS adjustment envision that the power supplied to the processing component may be at the F_opt frequency and per a pulse width modulation (“PWM”) power management scheme. Advantageously, by supplying the power to the processing component at the F_opt frequency and per a PWM power management scheme, certain embodiments may optimize power consumption while the processing component is processing a block of code and optimize power savings after the block of code is processed as a power collapse period is maximized. 
     Additionally, some embodiments of the systems and methods for dynamic DCVS adjustment that supply power to the processing component at the F_opt frequency and per a pulse width modulation (“PWM”) power management scheme envision do so such that the average operating frequency supplied over a duty cycle equals a targeted frequency or F_req. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       In the drawings, like reference numerals refer to like parts throughout the various views unless otherwise indicated. For reference numerals with letter character designations such as “ 102 A” or “ 102 B”, the letter character designations may differentiate two like parts or elements present in the same figure. Letter character designations for reference numerals may be omitted when it is intended that a reference numeral to encompass all parts having the same reference numeral in all figures. 
         FIG. 1A  is a graph illustrating a pair of performance curves of an exemplary processing component operating under different thermal conditions; 
         FIG. 1B  is a graph illustrating a pair of performance curves of an exemplary processing component operating at a given thermal condition, under a light workload, and with an optimal power frequency; 
         FIG. 1C  is a graph illustrating performance curves of exemplary processing components operating under different thermal conditions, a light workload, and with different optimal power frequencies; 
         FIG. 1D  is a graph illustrating a pair of performance curves of an exemplary processing component operating at different frequencies under a pulse width modulation (“PWM”) power management technique; 
         FIG. 2A  is a graph illustrating a pair of performance curves of an exemplary processing component operating at a given thermal condition and subject to a thermal mitigation technique; 
         FIG. 2B  is a graph illustrating a pair of performance curves of an exemplary processing component operating subject to a thermal mitigation technique and at different frequencies, a minimum frequency versus an optimal frequency under a pulse width modulation (“PWM”) power management technique; 
         FIG. 3  is a functional block diagram illustrating an embodiment of an on-chip system for dynamic DCVS adjustment and workload scheduling in a portable computing device (“PCD”); 
         FIG. 4  is a functional block diagram illustrating an exemplary embodiment of the PCD of  FIG. 3 ; 
         FIG. 5  is a schematic diagram illustrating an exemplary software architecture of the PCD of  FIG. 4  for supporting dynamic DCVS adjustment and workload scheduling based on comparative analysis of processor performance curves; 
         FIGS. 6A-6B  is a logical flowchart illustrating an embodiment of a method for dynamic DCVS adjustment and pulse width modulation of power management to a processing component; 
         FIG. 7  is a logical flowchart illustrating an embodiment of a method for dynamic DCVS adjustment and pulse width modulation of power management to a processing component based on performance curves derived from static and dynamic measurements; and 
         FIG. 8  is a logical flowchart illustrating an exemplary embodiment of a method for workload scheduling based on a comparative analysis of processor performance curves and optimal power frequencies associated with a dynamic DCVS adjustment. 
     
    
    
     DETAILED DESCRIPTION 
     The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect described herein as “exemplary” is not necessarily to be construed as exclusive, preferred or advantageous over other aspects. 
     In this description, the term “application” may also include files having executable content, such as: object code, scripts, byte code, markup language files, and patches. In addition, an “application” referred to herein, may also include files that are not executable in nature, such as documents that may need to be opened or other data files that need to be accessed. 
     As used in this description, the terms “component,” “database,” “module,” “system,” “thermal energy generating component,” “processing component,” “processing engine” and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a computing device and the computing device may be a component. One or more components may reside within a process and/or thread of execution, and a component may be localized on one computer and/or distributed between two or more computers. In addition, these components may execute from various computer readable media having various data structures stored thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the Internet with other systems by way of the signal). 
     In this description, the terms “central processing unit (“CPU”),” “digital signal processor (“DSP”),” “chip,” “video codec,” “system bus,” “image processor,” and “media display processor (“MDP”)” are non-limiting examples of processing components that are controllable through dynamic clock and voltage scaling (“DCVS”) techniques and may reside in a PCD. These terms for processing components are used interchangeably except when otherwise indicated. Moreover, as distinguished in this description, any of the above or their equivalents may be comprised of one or more distinct processing components generally referred to herein as “core(s)” and “sub-core(s).” 
     In this description, it will be understood that the terms “thermal” and “thermal energy” may be used in association with a device or component capable of generating or dissipating energy that can be measured in units of “temperature.” Consequently, it will further be understood that the term “temperature,” with reference to some standard value, envisions any measurement that may be indicative of the relative warmth, or absence of heat, of a “thermal energy” generating device or component. For example, the “temperature” of two components is the same when the two components are in “thermal” equilibrium. 
     In this description, the terms “workload,” “process load,” “process workload” and “block of code” are used interchangeably and generally directed toward the processing burden, or percentage of processing burden, that is associated with, or may be assigned to, a given processing component in a given embodiment. Further to that which is defined above, a “processing component” or “thermal energy generating component” or “thermal aggressor” may be, but is not limited to, a central processing unit, a graphical processing unit, a core, a main core, a sub-core, a processing area, a hardware engine, etc. or any component residing within, or external to, an integrated circuit within a portable computing device. Moreover, to the extent that the terms “thermal load,” “thermal distribution,” “thermal signature,” “thermal processing load” and the like are indicative of workload burdens that may be running on a processing component, one of ordinary skill in the art will acknowledge that use of these “thermal” terms in the present disclosure may be related to process load distributions, workload burdens and power consumption. 
     In this description, the terms “thermal mitigation technique(s),” “thermal policies,” “thermal management” and “thermal mitigation measure(s)” are used interchangeably. 
     One of ordinary skill in the art will recognize that the term “MIPS” represents the number of millions of instructions per second a processor is able to process at a given power frequency. In this description, the term is used as a general unit of measure to indicate relative levels of processor performance in the exemplary embodiments and will not be construed to suggest that any given embodiment falling within the scope of this disclosure must, or must not, include a processor having any specific Dhrystone rating or processing capacity. Additionally, as would be understood by one of ordinary skill in the art, a processor&#39;s MIPS setting directly correlates with the power frequency being supplied to the processor. 
     In this description, the term “portable computing device” (“PCD”) is used to describe any device operating on a limited capacity power supply, such as a battery. Although battery operated PCDs have been in use for decades, technological advances in rechargeable batteries coupled with the advent of third generation (“3G”) and fourth generation (“4G”) wireless technology have enabled numerous PCDs with multiple capabilities. Therefore, a PCD may be a cellular telephone, a satellite telephone, a pager, a PDA, a smartphone, a navigation device, a smartbook or reader, a media player, a combination of the aforementioned devices, a laptop computer with a wireless connection, among others. 
     Managing processing performance for QoS optimization in a PCD that has a heterogeneous processing component(s) can be accomplished by leveraging the diverse performance characteristics of the individual processing engines that are available for workload allocation. With regards to the diverse performance characteristics of various processing engines that may be included in a heterogeneous processing component, one of ordinary skill in the art will recognize that performance differences may be attributable to any number of reasons including, but not limited to, differing levels of silicon, design variations, etc. Moreover, one of ordinary skill in the art will recognize that the performance characteristics associated with any given processing component may vary in relation with the operating temperature of that processing component, the power level supplied to that processing component, etc. 
     For instance, consider an exemplary heterogeneous multi-core processor which may include a number of different processing cores generally ranging in performance capacities from low to high (notably, one of ordinary skill in the art will recognize that an exemplary heterogeneous multi-processor system on a chip (“SoC”) which may include a number of different processing components, each containing one or more cores, may also be considered). As would be understood by one of ordinary skill in the art, a low performance to medium performance processing core within the heterogeneous processor will exhibit a lower power leakage rate at a given workload capacity, and consequently a lower rate of thermal energy generation, than a processing core having a relatively high performance capacity. The higher capacity core may be capable of processing a given number of MIPS in a shorter amount of time than a lower capacity core. Similarly, a high capacity core with a processing speed that has been wilted may exhibit a lower power leakage rate at a given workload capacity, and consequently a lower rate of thermal energy generation, than when processing at its full, unchecked capacity. 
     Even so, depending on the thermal conditions under which the cores may be operating, the lower performance core may be more, or less, efficient (in power consumption) at processing the given number of MIPS than a high performance core. As such, by considering the individual performance curves of the diverse cores within the heterogeneous processor, where the performance curves indicate the power consumed by a given core at a given operating temperature in order to process a given number of MIPS, a dynamic DCVS adjustment algorithm can be leveraged to set the power frequency for each core such that a ratio of processed MIPS/power consumption (power efficiency ratio) is optimized. Notably, and as one of ordinary skill in the art would recognize, the power efficiency ratio may be quantified as operating frequency/power consumption. The power efficiency ratios may then be compared so that a scheduling component allocates workload to the processor best positioned to efficiently process the workload. 
     Additionally, it is envisioned that certain embodiments of a dynamic DCVS adjustment algorithm may implement a pulse width modulation (“PWM”) power management scheme to the processor(s) such that the processor(s) modulate between a power collapsed state and a processing state running at the optimal power frequency. Advantageously, in embodiments of a dynamic DCVS system and method that include PWM based on the optimal frequency, power consumption is optimized when the processor is processing and power savings are optimized when the processor is collapsed. 
     Notably, although exemplary embodiments of the systems and methods are illustrated and described herein in the context of individual processing components, it is envisioned that embodiments of the systems and methods may average the performance curves of multiple processing components (such as cores in a multi-core CPU) to arrive at a single average processing curve. Based on the single, average processing curve of the multiple processing components, an optimum frequency may be determined and applied to each core such that the power density across the entire processing component is optimized even though the processing efficiencies associated with individual cores may not be optimized. Such an embodiment may be applied in connection with a synchronized parallel processing workload on a multi-core processing component, for example. 
     As a non-limiting example of an application for a dynamic DCVS method, a particular block of code may be processed by either of a central processing unit (“CPU”) or a graphical processing unit (“GPU”) within an exemplary PCD. Advantageously, instead of predetermining that the particular block of code will be processed by one of the CPU or GPU, an exemplary embodiment may select which of the processing components will be assigned the task of processing the block of code as the need for processing the code ripens. That is, a “snap shot” of the performance curves of the CPU and GPU running at their optimal frequencies may be compared so that the processor best equipped to efficiently process the block of code is assigned the workload. Notably, it will be understood that subsequent processor selections for allocation of subsequent workloads may be made in real time, or near real time, as the blocks of code exit a scheduling queue. In this way, a frequency selection module may leverage operating temperatures associated with individual cores in a heterogeneous processor to optimize QoS by selecting the power frequencies that are most optimal for processing workloads and then selecting processing cores just prior to workload allocation. 
       FIG. 1A  is a graph  300  illustrating a pair of performance curves (Core 85° C., Core 50° C.) of an exemplary processing component operating under different thermal conditions. The processing component may be a core within a heterogeneous multi-core processor and may be a high capacity, medium capacity or low capacity core. More specifically, as one of ordinary skill in the art will acknowledge, the processing component may be any processing engine capable of processing a given block of code including, but not limited to, a CPU, GPU, DSP, programmable array, video encoder/decoder, system bus, camera sub-system (image processor), MDP, etc. Moreover, as described above, the exemplary processing engine may be a core or sub-core within a CPU, GPU, etc. 
     As can be seen from the  FIG. 1A  illustration, at a workload of 3500 MIPS the exemplary core operating in a 50° C. environment consumes approximately 620 mW of power (point  315 ) but, at the same 3500 MIPS workload, the power consumption of the core increases to almost 1000 mW of power (point  310 ) when the operating environment reaches 85° C. Similarly, for a given operating temperature, the processing efficiency of a core decreases with an increase in workload. Referring to the Core 50° C. curve, for example, when the workload is increased from 3500 MIPS to approximately 4300 MIPS, the power consumption increases to almost 1000 mW (point  305 ). 
     It can be seen from the  FIG. 1A  illustration that, for a given processing component, the efficiency of the processing component in terms of power consumption decreases as the operating temperature rises (i.e., as the operating temperature of a processing component increases, the number of MIPS it is capable of processing at a given operating frequency will decrease). Notably, one of ordinary skill in the art will recognize that a rise in operating temperature of an exemplary processing component may be caused by any number of factors or combination of factors including, but not limited to, increased power leakage within the processing component associated with higher clocking speeds, thermal aggressors adjacent to the processing component, malfunctioning components adjacent to the processing component, change of ambient environment, etc. Moreover, one of ordinary skill in the art will recognize that increased workloads on a processing component may cause the operating temperature associated with the processing component at the time of workload allocation to rise as a result of an increased power leakage rate associated with an increase in power consumption. Regardless of why the operating temperature of a processing component may rise or fall, it is important to note from the  FIG. 1A  illustration that, in general, the processing efficiency of a given processing component decreases inversely with an increase in operating temperature. 
     Turning now to  FIG. 1B , a graph  400  illustrating a pair of performance curves of an exemplary processing component operating at a given thermal condition, under a light workload, and with an optimal power frequency  410  is shown. One of ordinary skill in the art will recognize that the exemplary processor represented by the performance curve pair in  FIG. 1B  may be contained in a common heterogeneous multi-processor system on a chip (“SoC”). Alternatively, it is envisioned that the performance curve pair  400  may represent an average curve derived from multiple processing components. 
     The upper curve  400 A plots the performance of the exemplary processor when operating at a given temperature. As can be seen in the  FIG. 1B  illustration, the y-axis of the performance curve  400 A represents total power consumed by the exemplary processing component and the x-axis represents the processor operating frequency, i.e. the power frequency supplied to the processing component. A maximum power frequency, F_max, that may be supplied to the exemplary processing component is represented by point  415 A and a minimum power frequency, F_min, is represented by point  405 A. As one of ordinary skill in the art would understand, below F_min  405 A the exemplary processing component is power collapsed such that no power is being consumed. 
     As noted above, the processor operating frequency correlates with the MIPS capable of being processed by the given processor. Accordingly, for the exemplary processing component associated with the power curves  400  of the  FIG. 1B  illustration, the sheer volume of workload it can process is maximized at point  415 , i.e. when the maximum rated processing frequency is supplied to it. Notably, however, point  415 A does not represent the most power efficient point along curve  400 A. With this in mind, slopes  420 ,  430  and  425  depict the ratio of power consumption/operating frequency associated with each of points  405 A,  410 A and  415 A, respectively. From the slopes,  420 ,  430  and  425 , it can be seen that the exemplary processing component is most efficient along slope  430  which is tangential to performance curve  400 A at F_opt  410 A. Consequently, a system and method for dynamic DCVS adjustment may dictate that the power frequency provided to the exemplary processing component associated with the  FIG. 1B  curves be set at F_opt, thereby optimizing the amount of workload processed by the processor per milliwatt (“mW”) of power consumed. 
     The power efficiency represented by the frequency that corresponds to point  410 A can be seen in the complimentary performance curve  400 B. In the  400 B plot, the y-axis represents the energy efficiency of the slopes depicted in plot  400 A while the x-axis continues to represent processor operating frequency. As the slopes move up curve  400 A to intersect various points, the plot of  400 B indicates that, for the given operating temperature, the exemplary processing component is most energy efficient in processing workloads at point  410 B, i.e. when the power frequency supplied to it is F_opt. Consequently, embodiments of the systems and methods for dynamic DCVS adjustment may seek to adjust frequency settings on power supplies to match the F_opt contained in a performance curve associated with a given processor&#39;s operating temperature. 
     Building on the performance curves depicted in the  FIG. 1B  graph, the graph  500  of  FIG. 1C  illustrates performance curves of exemplary processing components operating under different thermal conditions, a light workload, and with different optimal power frequencies. In the graph  500 A, three exemplary performance curve pairs  501 ,  502  and  503  are shown plotted together. At point  512 , the cores associated with the curve pairs  501 ,  502  and  503  are power collapsed, i.e. no power is being consumed by the cores. For illustrative purposes, performance curves  501  may be associated with a high capacity core running at an operating temperature of 85° C., performance curves  502  may be associated with another high capacity core with similar characteristics to the core associated with curves  501  except it is running at an operating temperature of 50° C., and performance curves  503  may be associated with a low capacity core running at a relatively cool operating temperature of 90° C. 
     Considering the performance curve pairs individually, a light workload allocation may normally dictate a power frequency be supplied that is below the F_opt for the given core. For instance, a light workload that does not require particularly fast processing in order to provide a user with a high QoS may be allocated to the core associated with performance curves  501  and, in response to that allocation, a power management module may request that the power frequency supplied to that processor is F_req  511 A. In such a scenario, an embodiment of a dynamic DCVS adjustment method may adjust the power frequency up to F_opt so that the workload gets processed more efficiently, albeit processed more quickly than necessary in view of the QoS. Similarly, a power management module seeking to allocate a light workload to the core associated with performance curves  502  may request that the power frequency be set to point  511 B. An embodiment of a dynamic DCVS adjustment method may override the request to set the frequency at point  511 B in favor of the more efficient setting associated with F_opt  510 B. For the core associated with performance curves  503 , however, the power management module may request that the operating frequency be set at point  511 C in order to process the light workload without overly impacting QoS. In such a scenario, a dynamic DCVS adjustment method may allow the processing frequency to be set at point  511 C even though the processor associated with curves  503  is more efficient at the frequency represented by point  510 C. 
     Continuing with the performance curves depicted in the  FIG. 1C  graph  500 , certain embodiments of a dynamic DCVS adjustment method may compare the optimal processing frequencies  510  of each eligible core (e.g., the cores associated with performance curves  501 ,  502 ,  503 ). Based on the comparison from graph  500 B, and assuming that each processor may process the workload at its F_opt  510  without detrimentally impacting QoS, a dynamic DCVS adjustment method may determine that the processor associated with performance curves  503  and F_opt  510 C is best positioned to efficiently process the block of code. Accordingly, the block of code may be scheduled to the processor associated with curve pair  503  and the processor supplied power at a frequency equal to  510 C. 
     Furthering the example of an embodiment of a dynamic DCVS adjustment method that compares performance curves and F_opt frequencies of multiple eligible processing cores before selecting the core to which a block of code will be allocated, consider a workload that requires a fast processing speed in order to maintain a desirable QoS level (e.g., a gaming application). Further suppose that the required frequency is above F_opt  510 C but still below F_opts  510 A and  510 B. In such a scenario, an embodiment of a dynamic DCVS adjustment method may select the core associated with curves  502  as F_opt  510 B provides better energy efficiency than any point along curve  501 B. 
     Turning now to  FIG. 1D , a graph  520  illustrating a pair of performance curves of an exemplary processing component operating at different frequencies under a pulse width modulation (“PWM”) power management technique is shown. For exemplary purposes, suppose that an embodiment of a dynamic DCVS adjustment method assigned a workload to the core associated with performance curves  501  in  FIG. 1C . When the workload was assigned, in the interest of conserving power a power management module may have requested the power frequency be set to point  511 A, as the number of MIPS processed by the core at frequency  511 A would be adequate to maintain a satisfactory QoS. The dynamic DCVS adjustment method, however, may recognize that the power efficiency of the core associated with curves  501  is better at a frequency represented by F_opt  510 A, as described above. 
     It is envisioned that certain embodiments of a dynamic DCVS adjustment method and system may employ a PWM technique to further increase power efficiency. Continuing with the above example, if the core associated with curves  501  were set at the requested power frequency  511 A and run per a PWM technique, the processor will consume power for an amount of time T 1  before completing the workload and power collapsing  512  for a time PC 1 . If, however, the dynamic DCVS adjustment method set the power frequency to the optimal frequency  510 A instead of the requested  511 A, the energy consumed in order to process the workload would be less per MIPS and the workload would be processed in the shorter period T 2 . Additionally, further power savings may be realized as the processor stays in a power collapsed mode for the longer period PC 2  relative to PC 1 . 
       FIG. 2A  is a graph  550  illustrating a pair of performance curves of an exemplary processing component operating at a given thermal condition and subject to a thermal mitigation technique. It is envisioned that in some scenarios a thermal mitigation module may set a limit on a power budget allocated to a given processing component. In such a scenario, an optimal frequency such as F_opt  510 D may correspond to a power level that exceeds the power budget. 
     A dynamic DCVS adjustment system and method may use a PWM technique to operate the processing component in pulses of active durations at the F_opt frequency such that the average power level supplied over a given duty cycle equals the power budget. In this way, the processor may still be run at its most optimal frequency level while not causing the thermal mitigation power budget to be exceeded. Even though the F_opt  510 D corresponds to a power level that exceeds the power budget  560 , for example, any workload processed under a power budget that limits the frequency to something below F_opt  510 D, will be processed at F_opt  510 D on a PWM scheme. 
       FIG. 2B  includes graphs  580 A and  580 B that illustrate the effect of a dynamic DCVS adjustment method that utilizes a PWM scheme in conjunction with an F_opt setting. As described above, a thermal mitigation module may dictate that a certain power budget not be exceeded for a given processing component. In such case, for example, the frequency setting of the processing component may be set to F_min  555  as it correlates to a power level that is within the power budget. Advantageously, a dynamic DCVS adjustment system and method may override the F_min  555  setting in favor of an F_opt  510 D setting that is supplied to the processing component per a PWM scheme. Referring to the  580 B graph, by pulsing the power at the F_opt  510 D level, the power budget constraints set by the thermal mitigation module may be adhered to as the average frequency supplied over a given duty cycle equates to F_min  555 . 
       FIG. 3  is a functional block diagram illustrating an embodiment of an on-chip system  102  for dynamic DCVS adjustment and workload scheduling in a portable computing device (“PCD”)  100 . As explained above relative to the  FIGS. 1 and 2  illustrations, the workload allocation across the processing components may be based on a comparative analysis of performance curves and optimal frequency settings uniquely associated with the individual cores or processors  222 ,  224 ,  226 ,  228 . Notably, as one of ordinary skill in the art will recognize, the processing component(s)  110  is depicted as a group of heterogeneous processing engines for illustrative purposes only and may represent a single processing component having multiple, heterogeneous cores  222 ,  224 ,  226 ,  228  or multiple, heterogeneous processors  222 ,  224 ,  226 ,  228 , each of which may or may not comprise multiple cores and/or sub-cores. As such, the reference to processing engines  222 ,  224 ,  226  and  228  herein as “cores” will be understood as exemplary in nature and will not limit the scope of the disclosure. 
     The on-chip system may monitor temperature sensors  157  which are individually associated with cores  222 ,  224 ,  226 ,  228  with a monitor module  114  which is in communication with a frequency selection (“FS”) module  101  and a scheduler module  207 . The FS module  101  may receive temperature measurements from the monitor module  114  and use the measurements to query performance curves and determine optimum frequency settings. The dynamic DCVS adjustment policies dictated by the FS module  101  may set processor clock speeds at increased levels over requested speeds, apply PWM schemes to power supplies, select processor cores for workload allocation, etc. Notably, through application of dynamic DCVS adjustment policies, the FS module  101  may reduce or alleviate excessive power consumption at the cost of QoS. 
     As one of ordinary skill in the art will recognize, the operating temperature of one or more of the processing cores  222 ,  224 ,  226 ,  228  may fluctuate as workloads are processed, ambient conditions change, adjacent thermal energy generators dissipate energy, etc. Accordingly, as the operating temperatures of the various processing cores  222 ,  224 ,  226 ,  228  fluctuate, so do the performance curves associated those engines  222 ,  224 ,  226 ,  228 . As the operating temperatures associated with each of the cores  222 ,  224 ,  226 ,  228  change, the monitor module  114  recognizes the change and transmits temperature data indicating the change to both the FS module  101 . The change in measured operating temperatures may trigger the FS module  101  to reference a core performance (“CP”) data store  24  to query performance curves for one or more of the cores  222 ,  224 ,  226 ,  228  based on the measured operating temperatures. Subsequently, the FS module  101  may adjust the power frequency supplied to one or more of the cores  222 ,  224 ,  226 ,  228  so that it is operating at a frequency that delivers the most efficient processing of workload per milliwatt of power consumed. The FS module  101  may also compare the identified performance curves and adjust F_opts in order to select the core  222 ,  224 ,  226 ,  228  best positioned at the time of comparison to efficiently process a given block of code, similar to that which is depicted and described in the above Figures. 
     An exemplary FS module  101  is configured to leverage a comparative analysis of one or more performance curves associated with the various, diverse processing components  222 ,  224 ,  226 ,  228  to instruct the scheduler module  207  to allocate a workload to a certain processing component which is best positioned to efficiently process the workload. Notably, one of ordinary skill in the art will recognize that, as the operating temperatures of the processing components  222 ,  224 ,  226 ,  228  change, the performance curves queried and compared by the FS module  101  will also change. As such, at different times the FS module  101  may select different processing engines  222 ,  224 ,  226 ,  228  for allocation of repetitive or similar blocks of code. In this way, it is an advantage of certain embodiments that a FS module  101  ensures workload assignments are allocated to the most efficient processing components available at the time of allocation. 
       FIG. 4  is a functional block diagram of an exemplary, non-limiting aspect of a PCD  100  in the form of a wireless telephone for implementing methods and systems for monitoring thermal conditions, comparing performance curves, setting optimal power frequencies and scheduling workloads to processing components best positioned for efficient processing. As shown, the PCD  100  includes an on-chip system  102  that includes a heterogeneous multi-core central processing unit (“CPU”)  110  and an analog signal processor  126  that are coupled together. The CPU  110  may comprise a zeroth core  222 , a first core  224 , and an Nth core  230  as understood by one of ordinary skill in the art. Further, instead of a CPU  110 , a digital signal processor (“DSP”) may also be employed as understood by one of ordinary skill in the art. Moreover, as is understood in the art of heterogeneous multi-core processors, each of the cores  222 ,  224 ,  230  may process workloads at different efficiencies under similar operating conditions. 
     In general, the frequency selection module(s)  101  may receive temperature data from the monitor module  114  and use the temperature data to query performance curves associated with the cores  222 ,  224 ,  230 , determine optimal operating frequencies, perform a comparative analysis of the processing core performance curves and work with a scheduler  207  to schedule blocks of code to the cores  222 ,  224 ,  230  that will most efficiently process the workload. 
     The monitor module  114  communicates with multiple operational sensors (e.g., thermal sensors  157 ) distributed throughout the on-chip system  102  and with the CPU  110  of the PCD  100  as well as with the FS module(s)  101 . The FS module  101  may work with the monitor module  114  to query processor performance curves related to the temperatures monitored by the monitor module  114 , compare the curves, set the power frequencies to the most efficient levels, and select the most efficient processor available and capable of processing a block of code. 
     As illustrated in  FIG. 4 , a display controller  128  and a touchscreen controller  130  are coupled to the digital signal processor  110 . A touchscreen display  132  external to the on-chip system  102  is coupled to the display controller  128  and the touchscreen controller  130 . 
     PCD  100  may further include a video decoder  134 , e.g., a phase-alternating line (“PAL”) decoder, a sequential couleur avec memoire (“SECAM”) decoder, a national television system(s) committee (“NTSC”) decoder or any other type of video decoder  134 . The video decoder  134  is coupled to the multi-core central processing unit (“CPU”)  110 . A video amplifier  136  is coupled to the video decoder  134  and the touchscreen display  132 . A video port  138  is coupled to the video amplifier  136 . As depicted in  FIG. 4 , a universal serial bus (“USB”) controller  140  is coupled to the CPU  110 . Also, a USB port  142  is coupled to the USB controller  140 . A memory  112  and a subscriber identity module (SIM) card  146  may also be coupled to the CPU  110 . Further, as shown in  FIG. 4 , a digital camera  148  may be coupled to the CPU  110 . In an exemplary aspect, the digital camera  148  is a charge-coupled device (“CCD”) camera or a complementary metal-oxide semiconductor (“CMOS”) camera. 
     As further illustrated in  FIG. 4 , a stereo audio CODEC  150  may be coupled to the analog signal processor  126 . Moreover, an audio amplifier  152  may be coupled to the stereo audio CODEC  150 . In an exemplary aspect, a first stereo speaker  154  and a second stereo speaker  156  are coupled to the audio amplifier  152 .  FIG. 4  shows that a microphone amplifier  158  may be also coupled to the stereo audio CODEC  150 . Additionally, a microphone  160  may be coupled to the microphone amplifier  158 . In a particular aspect, a frequency modulation (“FM”) radio tuner  162  may be coupled to the stereo audio CODEC  150 . Also, an FM antenna  164  is coupled to the FM radio tuner  162 . Further, stereo headphones  166  may be coupled to the stereo audio CODEC  150 . 
       FIG. 4  further indicates that a radio frequency (“RF”) transceiver  168  may be coupled to the analog signal processor  126 . An RF switch  170  may be coupled to the RF transceiver  168  and an RF antenna  172 . As shown in  FIG. 4 , a keypad  174  may be coupled to the analog signal processor  126 . Also, a mono headset with a microphone  176  may be coupled to the analog signal processor  126 . Further, a vibrator device  178  may be coupled to the analog signal processor  126 .  FIG. 4  also shows that a power supply  180 , for example a battery, is coupled to the on-chip system  102 . In a particular aspect, the power supply includes a rechargeable DC battery or a DC power supply that is derived from an alternating current (“AC”) to DC transformer that is connected to an AC power source. 
     The CPU  110  may also be coupled to one or more internal, on-chip thermal sensors  157 A as well as one or more external, off-chip thermal sensors  157 B. The on-chip thermal sensors  157 A may comprise one or more proportional to absolute temperature (“PTAT”) temperature sensors that are based on vertical PNP structure and are usually dedicated to complementary metal oxide semiconductor (“CMOS”) very large-scale integration (“VLSI”) circuits. The off-chip thermal sensors  157 B may comprise one or more thermistors. The thermal sensors  157  may produce a voltage drop that is converted to digital signals with an analog-to-digital converter (“ADC”) controller  103 . However, other types of thermal sensors  157  may be employed without departing from the scope of the invention. 
     The thermal sensors  157 , in addition to being controlled and monitored by an ADC controller  103 , may also be controlled and monitored by one or more FS module(s)  101 . The FS module(s)  101  may comprise software which is executed by the CPU  110 . However, the FS module(s)  101  may also be formed from hardware and/or firmware without departing from the scope of the invention. The FS module(s)  101  may be responsible for querying processor performance curves and, based on an analysis of those curves, setting the power frequencies to an optimal levels and assigning blocks of code to processors most capable of efficiently processing the code at the time of workload allocation. 
     Returning to  FIG. 4 , the touchscreen display  132 , the video port  138 , the USB port  142 , the camera  148 , the first stereo speaker  154 , the second stereo speaker  156 , the microphone  160 , the FM antenna  164 , the stereo headphones  166 , the RF switch  170 , the RF antenna  172 , the keypad  174 , the mono headset  176 , the vibrator  178 , thermal sensors  157 B, and the power supply  180  are external to the on-chip system  102 . However, it should be understood that the monitor module  114  may also receive one or more indications or signals from one or more of these external devices by way of the analog signal processor  126  and the CPU  110  to aid in the real time management of the resources operable on the PCD  100 . 
     In a particular aspect, one or more of the method steps described herein may be implemented by executable instructions and parameters stored in the memory  112  that form the one or more FS module(s)  101 . These instructions that form the FS module(s)  101  may be executed by the CPU  110 , the analog signal processor  126 , or another processor, in addition to the ADC controller  103  to perform the methods described herein. Further, the processors  110 ,  126 , the memory  112 , the instructions stored therein, or a combination thereof may serve as a means for performing one or more of the method steps described herein. 
       FIG. 5  is a schematic diagram illustrating an exemplary software architecture of the PCD of  FIG. 4  for supporting dynamic DCVS adjustment and workload scheduling based on comparative analysis of processor performance curves. Any number of algorithms may form or be part of at least one dynamic DCVS adjustment and scheduling algorithm that may be applied by the FS module  101  when certain thermal conditions exist and associated performance curves analyzed. 
     As illustrated in  FIG. 5 , the CPU or digital signal processor  110  is coupled to the memory  112  via a bus  211 . The CPU  110 , as noted above, may be a multiple-core, heterogeneous processor having N core processors. That is, the CPU  110  may include a first core  222 , a second core  224 , and an N th  core  230 . As is known to one of ordinary skill in the art, each of the first core  222 , the second core  224  and the N th  core  230  are available for supporting a dedicated application or program and, as part of a heterogeneous core, may provide differing levels of performance under similar thermal operating conditions. Alternatively, one or more applications or programs can be distributed for processing across two or more of the available heterogeneous cores. 
     The CPU  110  may receive commands from the FS module(s)  101  that may comprise software and/or hardware. If embodied as software, the FS module  101  comprises instructions that are executed by the CPU  110  that issues commands to other application programs being executed by the CPU  110  and other processors. 
     The first core  222 , the second core  224  through to the Nth core  230  of the CPU  110  may be integrated on a single integrated circuit die, or they may be integrated or coupled on separate dies in a multiple-circuit package. Designers may couple the first core  222 , the second core  224  through to the N th  core  230  via one or more shared caches and they may implement message or instruction passing via network topologies such as bus, ring, mesh and crossbar topologies. 
     Bus  211  may include multiple communication paths via one or more wired or wireless connections, as is known in the art. The bus  211  may have additional elements, which are omitted for simplicity, such as controllers, buffers (caches), drivers, repeaters, and receivers, to enable communications. Further, the bus  211  may include address, control, and/or data connections to enable appropriate communications among the aforementioned components. 
     When the logic used by the PCD  100  is implemented in software, as is shown in  FIG. 5 , it should be noted that one or more of startup logic  250 , management logic  260 , frequency selection and scheduling interface logic  270 , applications in application store  280  and portions of the file system  290  may be stored on any computer-readable medium for use by or in connection with any computer-related system or method. 
     In the context of this document, a computer-readable medium is an electronic, magnetic, optical, or other physical device or means that can contain or store a computer program and data for use by or in connection with a computer-related system or method. The various logic elements and data stores may be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In the context of this document, a “computer-readable medium” can be any means that can store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. 
     The computer-readable medium can be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, device, or propagation medium. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic) having one or more wires, a portable computer diskette (magnetic), a random-access memory (RAM) (electronic), a read-only memory (ROM) (electronic), an erasable programmable read-only memory (EPROM, EEPROM, or Flash memory) (electronic), an optical fiber (optical), and a portable compact disc read-only memory (CDROM) (optical). Note that the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, for instance via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory. 
     In an alternative embodiment, where one or more of the startup logic  250 , management logic  260  and perhaps the frequency selection and scheduling interface logic  270  are implemented in hardware, the various logic may be implemented with any or a combination of the following technologies, which are each well known in the art: a discrete logic circuit(s) having logic gates for implementing logic functions upon data signals, an application specific integrated circuit (ASIC) having appropriate combinational logic gates, a programmable gate array(s) (PGA), a field programmable gate array (FPGA), etc. 
     The memory  112  is a non-volatile data storage device such as a flash memory or a solid-state memory device. Although depicted as a single device, the memory  112  may be a distributed memory device with separate data stores coupled to the digital signal processor (or additional processor cores). 
     The startup logic  250  includes one or more executable instructions for selectively identifying, loading, and executing a select program for frequency setting and comparative analysis and selection of one or more of the available cores such as the first core  222 , the second core  224  through to the N th  core  230 . 
     The management logic  260  includes one or more executable instructions for terminating a frequency setting and performance curve analysis program, as well as selectively identifying, loading, and executing a more suitable replacement program for frequency setting, comparative analysis, selection and workload allocation to one or more of the available cores. The management logic  260  is arranged to perform these functions at run time or while the PCD  100  is powered and in use by an operator of the device. A replacement program can be found in the program store  296  of the embedded file system  290 . 
     The replacement program, when executed by one or more of the core processors in the digital signal processor, may operate in accordance with one or more signals provided by the FS module  101 . In this regard, the modules  114  may provide one or more indicators of events, processes, applications, resource status conditions, elapsed time, temperature, etc. in response to control signals originating from the FS module  101 . 
     The interface logic  270  includes one or more executable instructions for presenting, managing and interacting with external inputs to observe, configure, or otherwise update information stored in the embedded file system  290 . In one embodiment, the interface logic  270  may operate in conjunction with manufacturer inputs received via the USB port  142 . These inputs may include one or more programs to be deleted from or added to the program store  296 . Alternatively, the inputs may include edits or changes to one or more of the programs in the program store  296 . Moreover, the inputs may identify one or more changes to, or entire replacements of one or both of the startup logic  250  and the management logic  260 . 
     The interface logic  270  enables a manufacturer to controllably configure and adjust an end user&#39;s experience under defined operating conditions on the PCD  100 . When the memory  112  is a flash memory, one or more of the startup logic  250 , the management logic  260 , the interface logic  270 , the application programs in the application store  280  or information in the embedded file system  290  can be edited, replaced, or otherwise modified. In some embodiments, the interface logic  270  may permit an end user or operator of the PCD  100  to search, locate, modify or replace the startup logic  250 , the management logic  260 , applications in the application store  280  and information in the embedded file system  290 . The operator may use the resulting interface to make changes that will be implemented upon the next startup of the PCD  100 . Alternatively, the operator may use the resulting interface to make changes that are implemented during run time. 
     The embedded file system  290  includes a hierarchically arranged core performance data store  24 . In this regard, the file system  290  may include a reserved section of its total file system capacity for the storage of information associated with the performance curves of the various cores  222 ,  224 ,  226 ,  228  at various operating temperatures. 
       FIGS. 6A-6B  is a logical flowchart illustrating an embodiment of a method  600  for dynamic DCVS adjustment and pulse width modulation of power management to a processing component. In the  FIG. 6  embodiment, the performance curves for each of the various processing cores  222 ,  224 ,  226 ,  228  may be empirically determined based on actual performance data gathered by the monitoring module  114  or, in some embodiments, the performance curves may be a priori curves driven by the performance specs of each core. 
     In some embodiments, to empirically determine the performance curves of the various processing cores  222 ,  224 ,  226 ,  228 , the monitoring module  114  may be in communication with temperature sensors  157  as well as various other voltage or current sensors useful for monitoring the power consumption of the cores  222 ,  224 ,  226 ,  228 . In such an embodiment, one of ordinary skill in the art will recognize that data gathered by the monitor module  114  may be coupled with previous workload allocation data received from the FS module  101  and compiled into empirical performance curves. The empirical performance curves may be stored in the CP data store  24  and later referenced by a frequency selection and workload scheduling algorithm. 
     Beginning at block  605 , the FS module  101  and/or monitor module  114  may receive or recognize a request to set a processing component&#39;s frequency to a required level, F_req, for processing a given workload. As explained above, the F_req frequency setting may be driven by a power management module seeking to minimize power consumption or a thermal mitigation module seeking to cap a power budget. At block  610 , the FS module  101  may request and receive from the monitor module  114  the operating temperature of the processing component such as, for example, the operating temperature associated with one of cores  222 ,  224 ,  226 ,  228 . At block  615 , the operating temperature of the given core may be used by the FS module  101  to query the CP data store  24  for applicable performance curves. 
     At block  620 , the FS module  101  may determine the optimal frequency setting, F_opt, for the given core when operating at the measured operating temperature. At decision block  625 , if the F_req is greater than the F_opt, then the FS module  101  may authorize the power frequency for the given core to be set at the requested frequency, F_req. If, however, at decision block  625  it is determined by the FS module  101  that the F_opt is greater than the F_req, then the process proceeds to decision block  635 . 
     If at decision block  635  the FS module  101  determines that the F_req frequency level is not dictated by a capped power budget, such as may have been implemented by a thermal mitigation module, then the “no” branch is followed to block  655  and the frequency is set to the optimum frequency, F_opt. Further, at block  655  certain embodiments of the system and method may cause the power supplied to the processor core at the F_opt level to be supplied per a PWM scheme. Advantageously, by setting the frequency level at the F_opt instead of the F_req, and by operating the processing component per a PWM scheme, embodiments of the systems and methods may cause workloads to be processed in the most power efficient manner available. 
     Returning to decision block  635 , if the F_req is dictated by a capped power budget for the given processor, the “yes” branch is followed to block  640 . At block  640 , the FS module  101  may determine from the performance curves the effective power frequency, F_effect, that the processor could run at without exceeding the power budget. Next, at block  645 , power frequencies below the F_opt may be disabled such that the lowest available power frequency for supply to the processor is equal to the F_opt. At block  650 , the FS module  101  may modulate the duty cycle of the processor per a PWM scheme between the F_opt power frequency and a power collapse state. In this way, the average power frequency supplied to the processor may equate to the F_effect. The process then returns to block  605 . 
     Notably, it is envisioned that at blocks  650  and  655  the duty cycle period of the PWM power management scheme may be statically or dynamically optimized to minimize the inrush current which is proportional to the total power rail capacitance and the voltage step delta. A longer period in the duty cycle may provide less steady performance but have a relatively smaller inrush current as compared to a shorter period that provides more steady performance at a higher inrush current level (see  FIGS. 1D and 2B ). 
       FIG. 7  is a logical flowchart illustrating an embodiment of a method  700  for dynamic DCVS adjustment and pulse width modulation of power management to a processing component based on performance curves derived from static and dynamic measurements. In the  FIG. 7  embodiment, the performance curves for each of the various processing cores  222 ,  224 ,  226 ,  228  may be empirically derived based on actual performance data gathered by the monitoring module  114 . In some embodiments, to empirically determine the performance curves of the various processing cores  222 ,  224 ,  226 ,  228 , the monitoring module  114  may be in communication with temperature sensors  157  as well as various other voltage or current sensors useful for monitoring the power consumption of the cores  222 ,  224 ,  226 ,  228 . In such an embodiment, one of ordinary skill in the art will recognize that data gathered by the monitor module  114  may be coupled with previous workload allocation data received from the FS module  101  and compiled into empirical performance curves. The empirical performance curves may be stored in the CP data store  24  and later referenced by a frequency selection and workload scheduling algorithm. 
     Beginning at block  705 , the FS module  101  and/or monitor module  114  may receive or recognize measurement data associated with static factors of the processing component. Static factors monitored by the monitor module  114  at block  705  may be read from various hardware components in the PCD  100  and may include, but are not limited to including, quiescent current leakage (“IDDQ”), process variation scaling (“PVS”) settings, speed bin settings, etc. Next, at block  710  the monitor module  114  may receive or recognize measurement data associated with dynamic factors of the processing component. Dynamic factors monitored by the monitor module  114  at block  710  may include, but are not limited to including, temperature and power supply voltage margin of the processing component. 
     Next, at block  715 , real-time performance curve data may be derived from the static and dynamic factors measured at blocks  705  and  710 . From the derived performance curves, an optimal frequency where the processing component is most efficient in terms of MIPS processed per mW of power consumed may be determined. Subsequently, at block  720 , a required frequency and a maximum frequency may be calculated. As explained above, the required frequency, F_req., may be the minimum frequency necessary for the processing component to process a given workload and meet a certain QoS level. Similarly, the maximum frequency, F_limit, may be the maximum sustainable power frequency that may be supplied to the processing component without exceeding a thermal power budget. 
     Using the F_opt, F_req, and F_limit, the dynamic DCVS adjustment algorithm may at block  725  set the power frequency supplied to the processing component according to the following conditions: 
     
       
         
           
               
               
             
               
                   
               
               
                 Condition 
                 Set power frequency supplied to: 
               
               
                   
               
             
            
               
                 F_limit &gt; F_req &gt; F_opt 
                 F_req 
               
               
                 F_limit &gt; F_opt &gt; F_req 
                 F_opt (in some embodiments, frequency 
               
               
                   
                 levels below F_opt may be temporarily 
               
               
                   
                 disabled) 
               
               
                 F_req &gt; F_limit &gt; F_opt 
                 F_limit 
               
               
                 F_req &gt; F_opt &gt; F_limit 
                 F_opt and operate per PWM mode so that 
               
               
                   
                 average effective frequency is equal 
               
               
                   
                 to F_limit 
               
               
                 F_opt &gt; F_limit &gt; F_req 
                 F_opt (in some embodiments, frequency 
               
               
                   
                 levels below F_opt may be temporarily 
               
               
                   
                 disabled) 
               
               
                 F_opt &gt; F_req &gt; F_limit 
                 F_opt and operate per PWM mode so that 
               
               
                   
                 average effective frequency is equal to 
               
               
                   
                 F_limit 
               
               
                   
               
            
           
         
       
     
       FIG. 8  is a logical flowchart illustrating an exemplary embodiment of a method  800  for workload scheduling based on a comparative analysis of processor performance curves and optimal power frequencies associated with a dynamic DCVS adjustment. Beginning at block  805 , the FS module  101  may receive a workload scheduling request from the scheduler  207 . At block  810 , the FS module  101  may work with the monitor module  114  to receive operating temperature data associated with processing components eligible to process the workload. In some embodiments, the FS module may also receive static and dynamic measurement data to derive performance curves and determine F_opts for each eligible processing component. 
     At block  815 , based on the received data the FS module  101  may query the CP store  24  for the performance curves associated with each eligible processing component or may derive performance curves. From the performance curves, at block  820  the optimal power frequency, F_opt, for each eligible processing component may be determined. Subsequently, at block  825 , the power efficiency associated with each F_opt may be compared to determine the processor best positioned to efficiently process the workload. Next, at block  830  the power frequency for the selected processor may be set to its F_opt previously determined at block  820  and the scheduler  207  instructed to allocate the workload to the selected processor. 
     Notably, it is envisioned that some embodiments may implement the  FIG. 7  method  700  for each of the one or more eligible processing components in method  800  and then, based on the power frequency setting determined for each eligible component, select the component best able to efficiently process the given workload. Further, one of ordinary skill in the art will recognize that the exemplary methods described herein may be applicable in SoCs having heterogeneous processing components or homogeneous processing components. Notably, because homogeneous processing components operating under different static and/or dynamic factors may exhibit different workload processing efficiencies, one particular homogeneous processing component may be better able to efficiently process a given workload than another eligible homogeneous processing component. 
     Certain steps in the processes or process flows described in this specification naturally precede others for the invention to function as described. However, the invention is not limited to the order of the steps described if such order or sequence does not alter the functionality of the invention. That is, it is recognized that some steps may performed before, after, or parallel (substantially simultaneously with) other steps without departing from the scope and spirit of the invention. In some instances, certain steps may be omitted or not performed without departing from the invention. Further, words such as “thereafter”, “then”, “next”, etc. are not intended to limit the order of the steps. These words are simply used to guide the reader through the description of the exemplary method. 
     Additionally, one of ordinary skill in programming is able to write computer code or identify appropriate hardware and/or circuits to implement the disclosed invention without difficulty based on the flow charts and associated description in this specification, for example. Therefore, disclosure of a particular set of program code instructions or detailed hardware devices is not considered necessary for an adequate understanding of how to make and use the invention. The inventive functionality of the claimed computer implemented processes is explained in more detail in the above description and in conjunction with the drawings, which may illustrate various process flows. 
     In one or more exemplary aspects, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media include both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that may be accessed by a computer. By way of example, and not limitation, such computer-readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to carry or store desired program code in the form of instructions or data structures and that may be accessed by a computer. 
     Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (“DSL”), or wireless technologies such as infrared, radio, and microwave, then the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave are included in the definition of medium. 
     Disk and disc, as used herein, includes compact disc (“CD”), laser disc, optical disc, digital versatile disc (“DVD”), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media. 
     Therefore, although selected aspects have been illustrated and described in detail, it will be understood that various substitutions and alterations may be made therein without departing from the spirit and scope of the present invention, as defined by the following claims.