You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


The present invention relates to the field of data processing. More particularly, this invention relates to the configuration of a processor core configured to carry out data processing operations.
Heterogeneous multicore systems—comprised of multiple cores with varying capabilities, performance, and energy characteristics—have emerged as a promising approach to increasing energy efficiency and alleviating serial bottlenecks. The big.LITTLE technology provided by ARM Limited, Cambridge, UK is one example. This technology combines a set of Cortex-A15 (“big”) cores with Cortex-A7 (“LITTLE”) cores to create a heterogeneous processor. The Cortex-A15 is a 3-way out-of-order device with deep pipelines (15-25 stages). Conversely, the Cortex-A7 is a narrow in-order processor with a relatively short pipeline (8-10 stages). The Cortex-A15 has 2-3× higher performance, but the Cortex-A7 is 3-4× more energy efficient. Such systems reduce energy consumption by identifying phase changes in an application and migrating execution to the most efficient core that meets its current performance requirements. Known designs select the best core by briefly sampling performance on each. However, every time the application migrates between cores, its current state must be explicitly transferred or rebuilt on the new core. This state transfer incurs large overheads that limits migration between cores to a granularity of tens to hundreds of millions of instructions. To mitigate these effects, the decision to migrate applications is done at the granularity of operating system time slices.
R. Kumar, K. I. Farkas, N. P. Jouppi, P. Ranganathan, and D. M. Tullsen, “Single-ISA Heterogeneous Multi-Core Architectures: The Potential for Processor Power Reduction,” in Proc. of the 36th Annual International Symposium on Microarchitecture, December 2003, pp. 81-92, considers migrating thread context between out-of-order and in-order cores for the purposes of reducing power. At coarse granularities of 100M instructions, one or more of the inactive cores are sampled by switching the thread to each core in turn. The switches comprise flushing dirty L1 data to a shared L2, which is slow and energy consuming.
Rather than relying on sampling the performance on both cores, K. Van Craeynest, A. Jaleel, L. Eeckhout, P. Narvaez, and J. Emer, “Scheduling heterogeneous multi-cores through performance impact estimation (pie),” in Proceedings of the 39th International Symposium on Computer Architecture, ser. ISCA '12, 2012, pp. 213-224 proposes a coarse-grained mechanism that relies on measures of CPI, MLP, and ILP to predict the performance on the inactive core. On the other hand, K. K. Rangan, G.-Y. Wei, and D. Brooks, “Thread motion: fine-grained power management for multi-core systems,” in Proc. of the 36th Annual International Symposium on Computer Architecture, 2009, pp. 302-313 examines a CMP with clusters of in-order cores sharing L1 caches. While the cores are identical architecturally, varied voltage and frequency settings create performance and power heterogeneity. A simple performance model is made possible by having exclusively in-order cores, and thread migration is triggered every 1000 cycles by a history-based (last value) predictor.
Another class of work targets the acceleration of bottlenecks to thread parallelism. Segments of code constituting bottlenecks are annotated by the compiler and scheduled at runtime to run on a big core. M. A. Suleman, O. Mutlu, M. K. Qureshi, and Y. N. Patt, “Accelerating critical section execution with asymmetric multi-core architectures,” in 17th International Conference on Architectural Support for Programming Languages and Operating Systems, 2009, pp. 253-264 describes a detailed architecture and target critical sections, and J. A. Joao, M. Suleman, O. Mutlu, and Y. N. Patt, “Bottleneck identification and scheduling in multithreaded applications,” in 20th International Conference on Architectural Support for Programming Languages and Operating Systems, 2012, pp. 223-234 generalizes this work to identify the most critical bottlenecks at runtime. G. Patsilaras, N. K. Choudhary, and J. Tuck, “Efficiently exploiting memory level parallelism on asymmetric coupled cores in the dark silicon era,” ACM Trans. Archit. Code Optim., vol. 8, no. 4, pp. 28:1-28:21, January 2012 proposes building separate cores, one that targets MLP and the other that targets ILP. They then use L2 cache miss rate to determine when an application has entered a memory intensive phase and map it to the MLP core. When the cache misses decrease, the system migrates the application back to the ILP core.
Other work studies the benefits of heterogeneity in real systems. M. Annavaram, E. Grochowski, and J. Shen, “Mitigating Amdahl's law through EPI throttling,” in Proceedings of the 32nd Annual International Symposium on Computer Architecture, 2005, pp. 298-309 shows the performance benefits of heterogeneous multi-cores for multithreaded applications on a prototype with different frequency settings per core. Y. Kwon, C. Kim, S. Maeng, and J. Huh, “Virtualizing performance asymmetric multi-core systems,” in Proc. of the 38th Annual International Symposium on Computer Architecture, 2011, pp. 45-56 motivates asymmetry-aware hypervisor thread schedulers, studying cores with various voltage and frequency settings. D. Koufaty, D. Reddy, and S. Hahn, “Bias scheduling in heterogeneous multi-core architectures,” in Proc. of the 5th European Conference on Computer Systems, 2010, pp. 125-138 discovers an application's big or little core bias by monitoring stall sources, to give preference to OS-level thread migrations which migrate a thread to a core it prefers. A heterogeneous multi-core prototype is produced by throttling the instruction retirement rate of some cores down to one instruction per cycle.
Other designs propose allowing a thread to adapt (borrow, lend, or combine) hardware resources, and still other designs allow dynamic voltage/frequency scaling (DVFS). Alternatively, asymmetry can be introduced by dynamically adapting a core's resources to its workload. Prior work has suggested adapting out-of-order structures such as the issue queue (see R. Bahar and S. Manne, “Power and energy reduction via pipeline balancing,” Proc. of the 28th Annual International Symposium on Computer Architecture, vol. 29, no. 2, pp. 218-229, 2001), as well as other structures such as ROBs, LSQs, and caches (see: D. Ponomarev, G. Kucuk, and K. Ghose, “Reducing power requirements of instruction scheduling through dynamic allocation of multiple datapath resources,” in Proc. of the 34th Annual International Symposium on Microarchitecture, December 2001, pp. 90-101; R. Balasubramonian, D. Albonesi, A. Buyuktosunoglu, and S. Dwarkadas, “Memory hierarchy reconfiguration for energy and performance in general-purpose processor architectures,” in Proceedings of the 33rd annual ACM/IEEE international symposium on Microarchitecture, 2000, pp. 245-257; and D. Albonesi, R. Balasubramonian, S. Dropsbo, S. Dwarkadas, E. Friedman, M. Huang, V. Kursun, G. Magklis, M. Scott, G. Semeraro, P. Bose, A. Buyuktosunoglu, P. Cook, and S. Schuster, “Dynamically tuning processor resources with adaptive processing,” IEEE Computer, vol. 36, no. 12, pp. 49-58, December 2003).
R. Kumar, N. Jouppi, and D. Tullsen, “Conjoined-core chip multiprocessing,” in Proc. of the 37th Annual International Symposium on Microarchitecture, 2004, pp. 195-206 explored how a pair of adjacent cores can share area expensive structures, while keeping the floorplan in mind H. Homayoun, V. Kontorinis, A. Shayan, T.-W. Lin, and D. M. Tullsen, “Dynamically heterogeneous cores through 3d resource pooling,” in Proc. of the 18th International Symposium on High-Performance Computer Architecture, 2012, pp. 1-12 examined how micro-architectural structures can be shared across 3D stacked cores. These techniques are limited by the structures they adapt and cannot for instance switch from an out-of-order core to an in-order core during periods of low ILP.
E. Ipek, M. Kirman, N. Kirman, and J. Martinez, “Core fusion: Accommodating software diversity in chip multiprocessors,” in Proc. of the 34th Annual International Symposium on Computer Architecture, 2007, pp. 186-197 and C. Kim, S. Sethumadhavan, M. S. Govindan, N. Ranganathan, D. Gulati, D. Burger, and S. W. Keckler, “Composable lightweight processors,” in Proceedings of the 40th Annual IEEE/ACM International Symposium on Microarchitecture, 2007, pp. 381-394 describe techniques to compose or fuse several cores into a larger core. While these techniques provide a fair degree of flexibility, a core constructed in this way is generally expected to have a datapath that is less energy efficient than if it were originally designed as an indivisible core of the same size.
DVFS approaches reduce the voltage and frequency of the core to improve the core's energy efficiency at the expense of performance. However, when targeted at memory-bound phases, this approach can be effective at reducing energy with minimal impact on performance. Similar to traditional heterogeneous multicore systems, the overall effectiveness of DVFS suffers from coarse-grained scheduling intervals in the millisecond range. In addition, providing independent DVFS settings for more than two cores is costly in terms of both area and energy. Despite these limitations, DVFS is still widely used in production processors today, and has for example been incorporated into the above-mentioned ARM big.LITTLE heterogeneous multicore system. Two competing techniques to enable fine-grained DVFS, fast on-chip regulators (see W. Kim, M. S. Gupta, G.-Y. Wei, and D. Brooks, “System level analysis of fast, per-core DVFS using on-chip switching regulators,” in Proc. of the 14th International Symposium on High-Performance Computer Architecture, 2008, pp. 123-134 and W. Kim, D. Brooks, and G.-Y. Wei, “A fully-integrated 3-level DCDC converter for nanosecond-scale DVFS,” IEEE Journal of Solid-State Circuits, vol. 47, no. 1, pp. 206-219, January 2012) and dual voltage rails (see T. N. Miller, X. Pan, R. Thomas, N. Sedaghati, and R. Teodorescu, “Booster: Reactive core acceleration for mitigating the effects of process variation and application imbalance in low-voltage chips,” in Proc. of the 18th International Symposium on High-Performance Computer Architecture, vol. 0, 2012, pp. 1-12 and R. Dreslinski, “Near threshold computing: From single core to manycore energy efficient architectures,” Ph.D. dissertation, University of Michigan, 2011), have recently been proposed that promise to deliver improved transition latencies.
Despite these varied advances in the technology, the applicant considers that there remains the opportunity to improve on the prior art.