Patent Application: US-89765210-A

Abstract:
methods and devices for automatically determining a suitable bit - width for data types to be used in computer resource intensive computations . methods for range refinement for intermediate variables and for determining suitable bit - widths for data to be used in vector operations are also presented . the invention may be applied to various computing devices such as cpus , gpus , fpgas , etc .

Description:
the challenges associated with scientific computing are addressed herein through a computational approach building on recent developments of computational techniques such as satisfiability - modulo theories ( smt ). this approach is shown on the right hand side of fig3 as the basic computational approach 306 . while this approach provides robust bit - widths even on ill - conditioned operations , it has limited scalability and is unable to directly address problems involving large vectors that arise frequently in scientific computing . given the critical role of computational bit - width allocation for designing hardware accelerators , detailed below is a method to automatically deal with problem instances based on large vectors . while the scalar based approach provides tight bit - widths ( provided that it is given sufficient time ), its computational requirements are very high . in response , a basic vector - magnitude model is introduced which , while very favorable in computational requirements compared with full scalar expansion , produces overly pessimistic bit - widths . subsequently there is introduced the concept of block vector , which expands the vector - magnitude model to include some directional information . due to its ability to tackle larger problems faster , we label this new approach as enhanced computational 307 in fig3 . this will enable effective scaling of the problem size , without losing the essential correlations in the dataflow , thus enabling automated bit - width allocation for practical applications . a robust computational method to address the range problem of custom data representation ; a means of scaling the computational method to vector based calculations ; a robust computational method to address the precision problem of custom data representation ; an error model for dealing with fixed - and floating - point representations in a unified way ; a robust computational method to address iterative scientific calculations . to illustrate the issues with prior art bit - width allocation methods , the following two examples are provided . let d and r be vectors εr 4 , where for both vectors , each component lies in the range [− 100 , 100 ]. suppose we have : and we want to determine the range of z for integer bit - width allocation . table 4 shows the ranges obtained from simulation , affine arithmetic and the proposed method . notice that simulation underestimates the range by 2 bits after 540 seconds , ≈ 5 × the execution time of the proposed method ( 98 seconds ). this happens because only a very small but still important fraction of the input space where d and r are identical ( to reduce z 2 ) and large ( to increase z 1 ) will maximize z . in contrast , the formal methods always give hard bounds but because the affine estimation of the range of the denominator contains zero , affine arithmetic cannot provide a range for the quotient z . in order to clarify the context of iterative scientific calculations and to demonstrate the necessity of formal methods , we discuss here , as a second example , an example based on newton &# 39 ; s method , an iterative scientific calculation widely employed in numerical methods . as an example , newton &# 39 ; s method is employed to determine the root of a polynomial : f ( x )= a 3 x 3 + a 2 x 2 + a 1 x + a 0 . the newton iteration : such calculations can arise in embedded systems for control when some characteristic of the system is modelled regressively using a 3 rd degree polynomial . ranges for the variables are given below : within the context of scientific computing , robust representations are required , an important criterion for many embedded systems applications . even for an example of modest size such as this one , if the range of each variable is split into 100 parts , the input space for simulation based methods would be 100 5 = 10 10 . the fact that this immense simulation would have to be performed multiple times ( to evaluate error ramifications of each quantization choice ), combined with the reality that simulation of this magnitude is still not truly exhaustive and thus cannot substantiate robustness clearly invalidates the use of simulation based methods . existing formal methods based on interval arithmetic and affine arithmetic also fail to produce usable results due to the inclusion of 0 in the denominator of z n but computational techniques are able to tackle this example . given the inability of simulation based methods to provide robust variable bounds , and the limited accuracy of bounds from affine arithmetic in the presence of strongly non - affine expressions , the following presents a method of range refinement based on sat - modulo theory . a two step approach is taken where loose bounds are first obtained from interval arithmetic , and subsequently refined using sat - modulo theory which is detailed below . boolean satisfiability ( sat ) is a well known np - complete problem which seeks to answer whether , for a given set of clauses in a set of boolean variables , there exists an assignment of those variables such that all the clauses are true . sat - modulo theory ( smt ) generalizes this concept to first - order logic systems . under the theory of real numbers , boolean variables are replaced with real variables and clauses are replaced with constraints . this gives rise to instances such as : is there an assignment of x , y , z εr for which x & gt ; 10 , y & gt ; 25 , z & lt ; 30 and z = x + y , which for this example there is not i . e ., this instance is unsatisfiable . instances are given in terms of variables with accompanying ranges and constraints . the solver attempts to find an assignment on the input variables ( inside the ranges ) which satisfies the constraints . most implementations follow a 2 - step model analogous to modern boolean sat solvers : 1 ) the decision step selects a variable , splits its range into two , and temporarily discards one of the sub - ranges then 2 ) the propagation step infers ranges of other variables from the newly split range . unsatisfiability of a subcase is proven when the range for any variable becomes empty which leads to backtracking ( evaluation of a previously discarded portion of a split ). the solver proceeds in this way until it has either found a satisfying assignment or unsatisfiability has been proven over the entire specified domain . building on this framework , an smt engine can be used to prove / disprove validity of a bound on a given expression by checking for satisfiability . noted below is how bounds proving can be used as the core of a procedure addressing the bounds estimation problem . fig5 illustrates the binary search method employed for range analysis on the intermediate variable : var . note that each smt instance evaluated contains the inserted constraint ( var & lt ; limit 500 or var & gt ; limit 501 ). the loop on the left of the figure ( between “ limit =( x1 + x2 )/ 2 ” 502 and “ x2 − x1 & lt ; thresh ?” 503 ) narrows in on the lower bound l , maintaining x1 less than the true ( and as yet unknown ) lower bound . each time satisfiability is proven 505 , x2 is updated while x1 is updated in cases of unsatisfiability 504 , until the gap between x1 and x2 is less than a user specified threshold . subsequently , the loop on the right performs the same search on the upper bound u , maintaining x2 greater than the true upper bound . since the smt solver 507 works on the exact calculation , all interdependencies among variables are taken into account ( via the smt constraints ) so the new bounds successively remove over - estimation arising in the original bounds resulting from the use of interval arithmetic . the overall range refinement process , illustrated in fig6 , uses the steps of the calculation and the ranges of the input variables as constraints to set up the base smt formulation , note that this is where insert constraint 500 from fig5 inserts to . it then iterates through the intermediate variables applying the process of fig5 to obtain a refined range for that variable . once all variables have been processed the algorithm returns ranges for the intermediate variables . as discussed above , non - affine functions with high curvature cause problems for aa , and while these are rare in the context of dsp ( as confirmed by the fang reference ) they occur frequently in scientific computing . division is in particular a problem due to range inversion i . e ., quotient increases as divisor decreases . while aa tends to give reasonable ( but still overestimated ) ranges for compounded multiplication since product terms and the corresponding affine expression grow in the same direction , this is not the case for division . furthermore , both ia and aa are unequipped to deal with divisors having a range that includes zero . use of smt mitigates these problems , divisions are formulated as multiplication constraints which must be satisfied by the sat engine , and an additional constraint can be included which restricts the divisor from coming near zero . since singularities such as division by zero result from the underlying math ( i . e . are not a result of the implementation ) their effects do not belong to range / precision analysis and smt provides convenient circumvention during analysis . while leveraging the mathematical structure of the calculation enables smt to provide much better run - times than using boolean sat ( where the entire data path and numerical constraints are modelled by clauses obfuscated the mathematical structure ), run - time may still become unacceptable as the complexity of the calculation under analysis grows . to address this , a timeout is used to cancel the inquiry if it does not return before timeout expiry ; see 506 in fig5 . in this way the trade - off between run - time and the tightness of the variables &# 39 ; bounds can be controlled by the user . if cancelled , inquiry result defaults along the same path as satisfiable in order to maintain robust bounds , i . e . to assume satisfiable gives pessimistic bounds . detailed below is a novel approach to bit - width allocation for operation in vector computations and vector calculus . in order to leverage the vector structure of the calculation and thereby reduce the complexity of the bit - width problem to the point of being tractable , the independence ( in terms of range / bit - width ) of the vector components as scalars is given up . at the same time , hardware implementations of vector based calculations typically exhibit the following : vectors are stored in memories with the same number of data bits at each address ; data path calculation units are allocated to accommodate the full range of bit - widths across the elements of the vectors to which they apply ; based on the above , the same number of bits tends to be used implicitly for all elements within a vector , which is exploited by the bit - width allocation problem . as a result , the techniques laid out below result in uniform bit - widths within a vector , i . e ., all the elements in a vector use the same representation , however , each distinct vector will still have a uniquely determined bit - width . the approach to dealing with problems specified in terms of vectors centers around the fact that no element of a vector can have ( absolute ) value greater than the vector magnitude i . e ., for a vector xεr n : starting from this fact , we can create from the input calculation a vector - magnitude model which can be used to obtain bounds on the magnitude of each vector , from which the required uniform bit - width for that vector can be inferred . creating the vector - magnitude model involves replacing elementary vector operations with equivalent operations bounding vector magnitude , table 7 contains the specific operations used in this document . when these substitutions are made , the number of variables for which bit - widths must be determined can be significantly reduced as well as the number of expressions , especially for large vectors . the entries of table 7 arise either as basic identities within , or derivations from , vector arithmetic . the first entry is simply one of the definitions of the standard inner product , and the addition and subtraction entries are resultant from the parallelogram law . the matrix multiplication entry is based on knowing the singular values σ i of the matrix and the pointwise product comes from : σx i 2 y i 2 ≦( σx i 2 ) ( σy i 2 ) while significantly reducing the complexity of the range determination problem , the drawback to using this method is that directional correlations between vectors are virtually unaccounted for . for example , vectors x and ax are treated as having independent directions , while in fact the true range of vector x + ax may be restricted due to the interdependence . in light of this drawback , below is proposed a means of restoring some directional information without reverting entirely to the scalar expansion based formulation . as discussed above , bounds on the magnitude of a vector can be used as bounds on the elements , with the advantage of significantly reduced computational complexity . in essence , the vector structure of the calculation ( which would be obfuscated by expansion to scalars ) is leveraged to speed up range exploration . these two methods of vector - magnitude and scalar expansion form the two extremes of a spectrum of approaches , as illustrated in fig8 . at the scalar side 800 , there is full description of interdependencies , but much higher computational complexity which limits how thoroughly one can search for the range limits 801 . at the vector - magnitude side 802 directional interdependencies are almost completely lost but computational effort is significantly reduced enabling more efficient use of range search effort 803 . a trade - off between these two extremes is made accessible through the use of block vectors 804 , which this section details . simply put , expanding the vector - magnitude model to include some directional information amounts to expanding from the use of one variable per vector ( recording magnitude ) to multiple variables per vector , but still fewer than the number of elements per vector . two natural questions arise : what information to store in those variables ? if multiple options of similar compute complexity exist , then how to choose the option that can lead to tighter bounds ? as an example to the above , consider a simple 3 × 3 matrix multiplication y = ax , where x , yεr 3 and x =[ x 0 , x 1 , x 2 ] t . fig9 shows an example matrix a ( having 9 . 9 ≦ σ a ≦ 50 . 1 ) 900 as well as constraints on the component ranges of x 901 . in the left case in the fig9 , the vector - magnitude approach is applied : ∥ x ∥ is bounded by √{ square root over (+ 2 2 + 2 2 + 10 2 )}= 10 . 4 903 , and the bound on ∥ y ∥ 904 is obtained as σ a max ∥ x ∥ which for this example is 50 . 1 × 10 . 4 ≈ 521 . the vector - magnitude estimate of ≈ 521 can be seen to be relatively poor , since true component bounds for matrix multiplication can be relatively easily calculated , ≈ 146 in this example . the inflation results from dismissal of correlations between components in the vector , which we address through the proposed partitioning into block vectors . the middle part of fig9 shows one partitioning possibility , around the component with the largest range , i . e . x 0 =[ x 0 , x 1 ] t , x 1 = x 2 906 . the input bounds now translate into a circle in the [ x 0 , x 1 ]- plane and a range on the x 2 - axis . the corresponding partitioning of the matrix is : where 10 . 4 ≦ σ a 00 ≦ 49 . 7 and 0 ≦ σ a 01 , σ a 10 ≦ 3 . 97 and simply a 11 = σ a 11 = 10 . 9 . using block vector arithmetic ( which bears a large degree of resemblance to standard vector arithmetic ) it can be shown that y 0 = a 00 x 0 + a 01 x 1 and y 1 = a 10 x 0 + a 11 x 1 . expanding each of these equations using vector - magnitude in accordance with the operations from table 7 , we obtain : as the middle part of fig9 shows , applying the vector - magnitude calculation to the partitions individually amounts to expanding the [ x 0 , x 1 ]- plane circle , and expanding the x 2 range , after which these two magnitudes can be recombined by taking the norm of [ y 0 , y 1 ] t to obtain the overall magnitude ∥ y ∥=√{ square root over (∥ y 0 ∥ 2 +∥ y 1 ∥ 2 )}. by applying the vector - magnitude calculation to the partitions individually , the directional information about the component with the largest range is taken into account . this yields | y 0 ∥≦≈ 183 907 and | y 1 ∥≦≈ 154 908 , thus producing the bound ∥ y ∥≦≈ 240 . the right hand portion of fig9 shows an alternative way of partitioning the vector , with respect to the basis vectors of the matrix a . decomposition of the matrix used in this example reveals the direction associated with the largest σ a to be very close to the x 0 axis . partitioning in this way ( i . e ., x 0 = x 0 , x 1 =[ x 1 , x 2 ] t 910 ) results in the same equations as above for partitioning around x 2 , but with different values for the sub - matrices : a 00 = σ a 00 = 49 . 2 and 0 ≦ σ a 01 , σ a 10 ≦ 5 . 61 and 10 . 8 ≦ σ a 11 ≦ 11 . 0 . as the figure shows , we now have range on the x 0 - axis and a circle in the [ x 1 , x 2 ]- plane which expands according the same rules ( with different numbers ) as before yielding this time : ∥ y 0 ∥≦≈ 137 911 and ∥ y 1 ∥≦≈ 124 912 producing a tighter overall magnitude bound ∥ y ∥≦≈ 185 . given the larger circle resulting from this choice of expansion , it may seem surprising the bounds obtained are tighter in this case . however , consider that in the x 0 direction ( very close to the direction of the largest σ a ), the bound is overestimated by 2 . 9 / 2 ≈ 1 . 5 in the middle case 905 while it is exact in the rightmost case 909 . contrast this to the overestimation of the magnitude in the [ x 1 , x 2 ]- plane of only about 2 % yet , different basis vectors for a could still reverse the situation . clearly the decision on how to partition a vector can have significant impact on the quality of the bounds . consequently , below is a discussion on an algorithmic solution for this decision . recall that in the cases from the middle and right hand portions of fig9 , the magnitude inflation is reduced , but for two different reasons . considering this fact from another angle , it can be restated that the initial range inflation ( which we are reducing by moving to block vectors ) arises as a result of two different phenomena ; 1 ) inferring larger ranges for individual components as a result of one or a few components with large ranges as in the middle case or 2 ) allowing any vector in the span defined by the magnitude to be scaled by the maximum σ a even though this only really occurs along the direction of the corresponding basis vector of a . in the case of magnitude overestimation resulting from one or a few large components , the impact can be quantified using range inflation : f vec ( x )=∥ x ∥/∥̂ x ∥, where ̂ x is x with the largest component removed . intuitively , if all the components have relatively similar ranges , this expression will evaluate to near unity , but removal of a solitary large component range will have a value larger than and farther from 1 , as in the example from fig9 ( middle ) of 10 . 4 / 2 . 9 = 3 . 6 turning to the second source of inflation based on σ a , we can similarly quantify the penalty of using σ a over the entire input span by evaluating the impact of removing a component associated with the largest σ a . if we define α as the absolute value of the largest component of the basis vector corresponding to σ a max , and â as the block matrix obtained by removing that same component &# 39 ; s corresponding row and column from a we can define : f mat ( a )= ασ â max / σ â max , where σ â max is the maximum across the σ max of each sub - matrix of â . the α factor is required to weight the impact of that basis vector as it relates to an actual component of the vector which a multiplies . when σ a max is greater than the other σ a , f mat will increase ( fig9 — right ) to 0 . 99 × 49 . 2 / 11 . 0 = 4 . 4 . note finally that the partition with the greater value ( 4 . 4 ≧ 3 . 6 ) produces the tighter magnitude bound ( 185 & lt ; 240 ). fig1 shows the steps involved in extracting magnitude bounds ( and hence bit - widths ) of a vector based calculation . taking the specification in terms of the calculation steps , and input variables and their ranges , vectormagnitudemodel on line 1 creates the base vector - magnitude model as above . the method then proceeds by successively partitioning the model ( line 5 ) and updating the magnitudes ( line 7 ) until either no significant tightening of the magnitude bounds occurs ( as defined by gainthresh in line 9 ) or until the model grows to become too complex ( as defined by sizethresh in line 10 ). note that the determineranges function ( line 7 ) is based upon bounds estimation techniques , the computational method from above ( although other techniques such as affine arithmetic may be substituted ). the partition function ( line 5 ) utilizes the impact functions f vec and f mat to determine a good candidate for partitioning . computing the f vec function for each input vector is inexpensive , while the f mat function involves two largest singular ( eigen ) value calculations for each matrix . it is worth noting however that a partition will not change the entire model , and thus many of the f vec and f mat values can be reused across multiple calls of partition . detailed below is an approach to solving the custom - representation bit - width allocation problem for general scientific calculations . an error model to unify fixed - point and floating - point representations is given first and a later section shows how a calculation is mapped under this model into smt constraints . following this , there is a discussion on how to break the problem for iterative calculations down using an iterative analysis phase into a sequence of analyses on direct calculations . fixed - point and floating - point representations are typically treated separately for quantization error analysis , given that they represent different modes of uncertainty : absolute and relative respectively . absolute error analysis applied to fixed - point numbers is exact however , relative error analysis does not fully represent floating - point quantization behaviour , despite being relied on solely in many situations . the failure to exactly represent floating - point quantization arises from the finite exponent field which limits the smallest magnitude , non - zero number which can be represented . in order to maintain true relative error for values infinitesimally close to zero , the quantization step size between representable values in the region around zero would itself have to be zero . zero step size would of course not be possible since neighbouring representable values would be the same . therefore , real floating - point representations have quantization error which behaves in an absolute way ( like fixed - point representations ) in the region of this smallest representable number . for this reason , when dealing with floating - point error tolerances , consideration must be given to the smallest representable number , as illustrated in the following example . for this example , consider the addition of two floating - point numbers : a + b = c with a , b ε [ 10 , 100 ], each having a 7 bit mantissa yielding relative error bounded by ≈ 1 %. the value of c is in [ 20 , 200 ], with basic relative error analysis for c giving 1 %, which in this case is reliable . consider now different input ranges : a , b ε [− 100 , 100 ], but still the same tolerances ( 7 bits ≈ 1 %). simplistic relative error analysis should still give the same result of 1 % for c however , the true relative error is unbounded . this results from the inability to represent infinitesimally small numbers , i . e . the relative error in a , b is not actually bounded by 1 % for all values in the range . a finite number of exponent bits limits the smallest representable number , leading to unbounded relative error for non - zero values quantized to zero . in the cases above , absolute error analysis would deal accurately with the error . however , it may produce wasteful representations with unnecessarily tight tolerances for large numbers . for instance , absolute error of 0 . 01 for a , b ( 7 fraction bits ) leads to absolute error of 0 . 02 for c , translating into a relative error bound of 0 . 01 % for c = 200 ( maybe unnecessarily tight ) and 100 % for c = 0 . 02 ( maybe unacceptably loose ). this tension between relative error near zero and absolute error far from zero motivates a hybrid error model . fig1 shows a unified absolute / relative error model dealing simultaneously with fixed - and floating - point numbers by restricting when absolute or relative error applies . error is quantified in terms of two numbers ( knee and slope as described below ) instead of just one as in the case of absolute and relative error analysis . put simply , the knee point 1100 divides the range of a variable into absolute and relative error regions , and slope indicates the relative error bound in the relative region ( above and to the right of the knee point ). below and to the left of the knee point , the absolute error is bounded by the value knee , the absolute error at the knee point ( which sits at the value knee / slope ). we thus define : knee : absolute error bound below the knee - point knee point = knee / slope : value at which error behaviour transitions from absolute to relative slope : fraction of value which bounds the relative error in the region above the knee point . of more significance than just capturing error behaviour of existing representations ( fixed - point 1101 , ieee - 754 single 1102 and double 1103 precision as in fig1 ), is the ability to provide more descriptive error constraints to the bit - width allocation than basic absolute or relative error bounds . through this model , a designer can explicitly specify desired error behaviour , potentially opening the door to greater optimization than possible considering only absolute or relative error alone ( which are both subsumed by this model ). bit - width allocation under this model is also no longer fragmented between fixed - and floating - point procedures . instead , custom representations are derived from knee and slope values obtained subject to the application constraints and optimization objectives . constructing precision constraints for an smt formulation will be discussed later , while translation of application objectives into such constraints is also covered below . the role of precision constraints is to capture the precision behaviour of intermediate variables , supporting the hybrid error model above . below we discuss derivation of precision expressions , as well as how to form constraints for the smt instance . we define four symbols relating to an intermediate ( e . g ., x ): 1 . the unaltered variable x stands for the ideal abstract value in infinite precision , 3 . δx stands for accumulated error due to finite precision representation , so x = x + δx , 4 . δx stands for the part of δx which is introduced during quantization of x . using these variables , the smt instance can keep track symbolically of the finite precision effects , and place constraints where needed to satisfy objectives from the specification . in order to keep the size of the precision ( i . e ., δx ) terms from exploding , atomic precision expressions can be derived at the operator level . for division for example , the infinite precision expression z = x / y is replaced with : what is shown above describes the operand induced error component of δz , measuring the reliability of z given that there is uncertainty in its operands . if next z were cast into a finite precision data type , this quantization results in : where δz captures the effect of the quantization . the quantization behaviour can be specified by what kind of constraints are placed on δz , as discussed in more detail below . the same process as for division above can be applied to many operators , scalar and vector alike . table 12 shows expressions and accompanying constraints for common operators . in particular the precision expression for square root as shown in row 7 of the table highlights a principal strength of the computational approach , the use of constraints rather than assignments . allowing only forward assignments would result in a more complicated precision expression ( involving use of the quadratic formula ), whereas the constraint - centric nature of smt instances permits a simpler expression . beyond the operators of table 12 , constraints for other operators can be derived under the same process shown for division , or operators can be compounded to produce more complex calculations . as an example , consider the quadratic form z = x t ax , for which error constraints can be obtained by explicit derivation ( starting from z = x t a x ) or by compounding an inner product with a matrix multiplication x t ( ax ). furthermore , constraints of other types can be added , for instance to capture the discrete nature of constant coefficients for exploitation similarly to the way they are exploited in the radecka reference . careful consideration must be given to the δ terms when capturing an entire dataflow into an smt formulation . in each place where used they must capture error behaviour for the entire part of the calculation to which they apply . for example , a matrix multiplication implemented using multiply accumulate units at the matrix / vector element level may quantize the sum throughout accumulation for a single element of the result vector . in this case it is not sufficient for the δz for matrix multiplication from table 12 to capture only the final quantization of the result vector into a memory location , the internal quantization effects throughout the operation of the matrix multiply unit must also be considered . what is useful about this setup is the ability to model off - the - shelf hardware units ( such as matrix multiply ) within our methodology , so long as there is a clear description of the error behaviour . also of importance for δ terms is how they are constrained to encode quantization behaviour . for fixed - point quantization , range is simply bounded by the rightmost fraction bit . for example , a fixed - point data type with 32 fraction bits and quantization by truncation ( floor ), would use a constraint of − 2 − 32 & lt ; δx ≦ 0 . similarly for rounding and ceiling , the constraints would be − 2 − 33 δx ≦ 2 − 33 and 0 ≦ δx & lt ; 2 − 32 respectively . these constraints are loose ( thus robust ) in the sense that they assume no correlation between quantization error and value , when in reality correlation may exist . if the nature of the correlation is known , it can be captured into constraints ( with increased instance complexity ). the tradeoff between tighter error bounds and increased instance complexity must be evaluated on an application by application basis ; the important point is that the smt framework provides the descriptive power necessary to capture known correlations . in contrast to fixed - point quantization , constraints for custom floating - point representations are more complex because error is not value - independent as seen in fig1 . the use of δ in the figure indicates either potential input error or tolerable output error of a representation , and in the discussion which follows the conclusions drawn apply equally to quantization if δ is replaced with δ . possibly the simplest constraint to describe this region indicated in fig1 would be : provided that the abs ( ) and max ( ) functions are supported by the smt solver . if not supported , and noting that another strength of the smt framework is the capacity to handle both numerical and boolean constraints , various constraint combinations exist to represent this region . fig1 shows numerical constraints which generate boolean values and the accompanying region of the error space in which they are true . from this , a number of constraints can be formed which isolate the region of interest . for example , note that the unshaded region of fig1 , where the constraint should be false , is described by : noting that c1 refers to boolean complementation and not finite precision as used elsewhere in this document and that & amp ; and | refer to the boolean and and or relations respectively . the complement of this produces a pair of constraints which define the region in which we are interested ( note that c3 and c4 are never simultaneously true ): while the one ( custom , in house developed ) solver used for our experiments does not support the abs ( ) and max ( ) functions , the other ( off - the - shelf solver , hysat ), does provide this support . even in this case however , the above boolean constraints can be helpful alongside the strictly numerical ones . providing extra constraints can help to speed the search by leading to a shorter proof since proof of unsatisfiability is what is required to give robustness [ see the kinsman and nicolici reference ]. it should also be noted that when different solvers are used , any information known about the solver &# 39 ; s search algorithm can be leveraged to set up the constraints in such a way to maximize search efficiency . finally , while the above discussion relates specifically to our error model , it is by no means restricted to it — any desired error behaviour which can be captured into constraints is permissible . this further highlights the flexibility gained by using the computational approach . detailed below is an approach which partitions iterative calculations into pieces which can be analyzed by the established framework . there are in general two main categories of numerical schemes for solving scientific problems : 1 ) direct where a finite number of operations leads to the exact result and 2 ) iterative where applying a single iteration refines an estimate of the final result which is converged upon by repeated iteration . fig1 shows one way how an iterative calculation may be broken into sub - calculations which are direct , as well as the data dependency relationships between the sub - calculations . specifically , the setup 1500 , core 1501 , auxiliary 1502 and takedown 1503 blocks of fig1 represent direct calculations , and done ? 1504 may also involve some computation , producing ( directly ) an affirmative / negative answer . the setup 1500 calculations provide ( based on the inputs 1505 ) initial iterates , which are updated by core 1501 and auxiliary 1502 until the termination criterion encapsulated by done 1504 is met , at which point post - processing to obtain the final result 1506 is completed by takedown 1503 , which may take information from core 1501 , auxiliary 1502 and setup 1500 . what distinguishes the core 1501 iteration from auxiliary 1502 is that core 1501 contains only the intermediate calculations required to decide convergence . that is , the iterative procedure for a given set of inputs 1505 will still follow the same operational path if the auxiliary 1502 calculations are removed . the reason for this distinction is twofold : 1 ) convergence analysis for core 1501 can be handled in more depth by the solver when calculations that are spurious ( with respect to convergence ) are removed , and 2 ) error requirements of the two parts may be more suited to different error analysis with the potential of leading to higher quality solutions . under the partitioning scheme of fig1 , the top level flow for determining representations is shown in fig1 . the left half of the figure depicts the partitioning and iterative analysis phases of the overall process , while the portion on the right shows bounds estimation and representation search applied to direct analysis . the intuition behind fig1 is as follows : at the onset , precision of inputs and precision requirements of outputs are known as part of the problem specification 1600 direct analysis 1601 on the setup 1500 stage with known input precision provides precision of inputs to the iterative stage direct analysis 1601 on the takedown 1503 stage with known output precision requirements provides output precision requirements of the iterative stage between the above , and iterative analysis 1602 leveraging convergence information , forward propagation of input errors and the backward propagation of output error constraints provide the conditions for direct analysis of the core 1501 and auxiliary 1502 iterations . building on the robust computational foundation established in the kinsman references , fig1 shows how smt is leveraged through formation of constraints for bounds estimation . also shown is the representation search 1603 which selects and evaluates candidate representations based on feedback from the hardware cost model 1604 and the bounds estimator 507 . the reason for the bidirectional relationship between iterative 1602 and direct 1601 analysis is to retain the forward / backward propagation which marks the smt method and thus to retain closure between the range / precision details of the algorithm inputs and the error tolerance limits imposed on the algorithm output . these iterative and direct analysis blocks will be elaborated in more detail below . as outlined above , the role of iterative analysis within the overall flow is to close the gap between forward propagation of input error , backward propagation of output error constraints and convergence constraints over the iterations . the plethora of techniques to provide detailed convergence information on iterative algorithms developed throughout the history of numerical analysis testify to the complexity of this problem and the lack of a one - size - fits all solution . not completely without recourse , one of the best aids that can be provided in our context of bit - width allocation is a means of extracting constraints which encode information specific to the convergence / error properties of the algorithm under analysis . a simplistic approach to iterative analysis would merely “ unroll ” the iteration , as shown in fig1 . a set of independent solver variables and dataflow constraints would be created for the intermediates of each iteration 1700 and the output variables of one iteration would feed the input of the next . while attractive in terms of automation ( iteration dataflow replication is easy ) and of capturing data correlations across iterations , the resulting instance 1701 can become large . as a result , solver run - times can explode , providing in reality very little meaningful feedback due to timeouts [ see the kinsman and nicolici reference ]. furthermore , conditional termination ( variable number of iterations ) can further increase instance complexity . for the reasons above , it is preferred to base an instance for the iterative part on a single iteration dataflow , augmented with extra constraints as shown in fig1 . the challenge then becomes determining those constraints 1800 which will ensure desired behaviour over the entire iterative phase of the numerical calculation . such constraints can be discovered within the theoretical analysis 1801 of the algorithm of interest . while some general facts surrounding iterative methods do exist ( e . g ., banach fixed point theorem in istratescu reference ), the guidance provided to the smt search by such facts is limited by their generality . furthermore , the algorithm designer / analyst ought ( in general ) to have greater insight into the subtleties of the algorithm than the algorithm user , including assumptions under which it operates correctly . to summarize the iterative analysis process , consider that use of smt essentially automates and accelerates reasoning about the algorithm . if any reasoning done offline by a person with deeper intuition about the algorithm can be captured into constraints , it will save the solver from trying to derive that reasoning independently ( if even it could ). in the next section , direct analysis is described , since it is at the core of the method with setup , takedown and the result of the iterative analysis phase all being managed through direct analysis . having shown that an iterative algorithm can be broken down into separate pieces for analysis as direct calculations , and noting that a large direct calculation can be partitioned into more manageable pieces according to the same reasoning , we discuss next in more detail this direct analysis depicted in the right half of fig1 . the constraints for the base formulation come from the dataflow and its precision counterpart involving δ terms , formed as discussed above . this base formulation includes constraints for known bounds on input error and requirements on output error . this feeds into the core of the analysis which is the error search across the δ terms ( discussed immediately below ), which eventually produces range limits ( as per kinsman reference ) for each intermediate , as well as for each δ term indicating quantization points within the dataflow which map directly onto custom representations . table 19 shows robust conversions from ranges to bit - widths in terms of i ( integer ), e ( exponent ), f ( fraction ), m ( mantissa ) and s ( sign ) bits . in each case , the sign bit is 0 if x l and x h have the same sign , or 1 otherwise . note that vector - magnitude bounds ( as per the kinsman reference ) must first be converted to element - wise bounds before applying the rules in the table , using ∥ x ∥ 2 & lt ; x →− x & lt ;[ x ] i & lt ; x , and ∥ δx ∥ 2 & lt ; x →− x /√{ square root over ( n )}& lt ;[ δx ] i & lt ; x /√{ square root over ( n )} for vectors with n elements . with the base constraints ( set up as above ) forming an instance for the solver , solver execution becomes the means of evaluating a chosen quantization scheme . the error search that forms the core of the direct analysis utilizes this error bound engine by plugging into the knee and slope values for each δ constraint . selections for knee and slope are made based on feedback from both the hardware cost model and the error bounds obtained from the solver . in this way , the quantization choices made as the search runs are meant to evolve toward a quantization scheme which satisfies the desired end error / cost trade - off . the method steps of the invention may be embodied in sets of executable machine code stored in a variety of formats such as object code or source code . such code is described generically herein as programming code , or a computer program for simplification . clearly , the executable machine code may be integrated with the code of other programs , implemented as subroutines , by external program calls or by other techniques as known in the art . it will now be apparent that the steps of the above methods can be varied and likewise that various specific design choices can be made relative to how to implement various steps in the methods . the embodiments of the invention may be executed by a computer processor or similar device programmed in the manner of method steps , or may be executed by an electronic system which is provided with means for executing these steps . similarly , an electronic memory means such computer diskettes , cd - roms , random access memory ( ram ), read only memory ( rom ), fixed disks or similar computer software storage media known in the art , may be programmed to execute such method steps . as well , electronic signals representing these method steps may also be transmitted via a communication network . embodiments of the invention may be implemented in any conventional computer programming language . for example , preferred embodiments may be implemented in a procedural programming language ( e . g . “ c ”), an object - oriented language ( e . g . “ c ++”), a functional language , a hardware description language ( e . g . verilog hdl ), a symbolic design entry environment ( e . g . matlab simulink ), or a combination of different languages . alternative embodiments of the invention may be implemented as pre - programmed hardware elements , other related components , or as a combination of hardware and software components . embodiments can be implemented as a computer program product for use with a computer system . such implementations may include a series of computer instructions fixed either on a tangible medium , such as a computer readable medium ( e . g ., a diskette , cd - rom , rom , or fixed disk ) or transmittable to a computer system , via a modem or other interface device , such as a communications adapter connected to a network over a medium . the medium may be either a tangible medium ( e . g ., optical or electrical communications lines ) or a medium implemented with wireless techniques ( e . g ., microwave , infrared or other transmission techniques ). the series of computer instructions embodies all or part of the functionality previously described herein . those skilled in the art should appreciate that such computer instructions can be written in a number of programming languages for use with many computer architectures or operating systems . furthermore , such instructions may be stored in any memory device , such as semiconductor , magnetic , optical or other memory devices , and may be transmitted using any communications technology , such as optical , infrared , microwave , or other transmission technologies . it is expected that such a computer program product may be distributed as a removable medium with accompanying printed or electronic documentation ( e . g ., shrink wrapped software ), preloaded with a computer system ( e . g ., on system rom or fixed disk ), or distributed from a server over the network ( e . g ., the internet or world wide web ). of course , some embodiments of the invention may be implemented as a combination of both software ( e . g ., a computer program product ) and hardware . still other embodiments of the invention may be implemented as entirely hardware , or entirely software ( e . g ., a computer program product ). a portion of the disclosure of this document contains material which is subject to copyright protection . the copyright owner has no objection to the facsimile reproduction by any one the patent document or patent disclosure , as it appears in the patent and trademark office patent file or records , but otherwise reserves all copyrights whatsoever . persons skilled in the art will appreciate that there are yet more alternative implementations and modifications possible for implementing the embodiments , and that the above implementations and examples are only illustrations of one or more embodiments . the scope , therefore , is only to be limited by the claims appended hereto . the references in the following list are hereby incorporated by reference in their entirety : a . b . kinsman and n . nicolici . computational bit - 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