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Flexible dsp accelerator architecture exploiting carry save arithmetic

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A High-Performance FIR Filter Architecture for Fixed and
Reconfigurable Applications
Flexible DSP Accelerator Architecture...
A High-Performance FIR Filter Architecture for Fixed and
Reconfigurable Applications
the use of CS arithmetic prior theact...
A High-Performance FIR Filter Architecture for Fixed and
Reconfigurable Applications
to the two’scomplement one. The regis...
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Flexible dsp accelerator architecture exploiting carry save arithmetic

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Flexible dsp accelerator architecture exploiting carry save arithmetic Flexible dsp accelerator architecture exploiting carry save arithmetic Flexible dsp accelerator architecture exploiting carry save arithmetic Flexible dsp accelerator architecture exploiting carry save arithmetic

Flexible dsp accelerator architecture exploiting carry save arithmetic Flexible dsp accelerator architecture exploiting carry save arithmetic Flexible dsp accelerator architecture exploiting carry save arithmetic Flexible dsp accelerator architecture exploiting carry save arithmetic

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Flexible dsp accelerator architecture exploiting carry save arithmetic

  1. 1. A High-Performance FIR Filter Architecture for Fixed and Reconfigurable Applications Flexible DSP Accelerator Architecture Exploiting Carry-Save Arithmetic Abstract: Hardware acceleration has been proved an extremelypromising implementation strategy for the digital signal processing (DSP)domain. Rather than adopting a monolithic application-specific integratedcircuit design approach, in this brief, we present a novel acceleratorarchitecture comprising flexible computational units that support theexecution of a large set of operation templates found in DSP kernels.We differentiate from previous works on flexible accelerators by enablingcomputations to be aggressively performed with carry-save (CS) formatteddata. Advanced arithmetic design concepts, i.e., recoding techniques,are utilized enabling CS optimizations to be performed in a larger scopethan in previous approaches.The proposed architecture of this paper analysis the logic size, area and power consumption using Xilinx 14.2. Enhancement of the project: Perform the other temple of the FCU. Existing system: Modern embedded systems target high-end application domainsrequiring efficient implementations of computationally intensivedigital signal processing (DSP) functions. The incorporation ofheterogeneity through specialized hardware accelerators improvesperformance and reduces energy consumption. Althoughapplication-specific integrated circuits (ASICs) form the ideal accelerationsolution in terms of performance and power, their inflexibilityleads to increased silicon complexity, as multiple instantiated ASICsare needed to accelerate various kernels. Many researchers haveproposed the use of domain-specific coarse-grained reconfigurable accelerators in order to increase ASICs’ flexibility withoutsignificantly compromising their performance. The aforementioned reconfigurable architectures excludearithmetic optimizations during the architectural synthesis andconsider them only at the internal circuit structure of primitivecomponents, e.g., adders, during the logic synthesis. However,research activities have shown that the arithmeticoptimizations at higher abstraction levels than the structuralcircuit one significantly impact on the datapath performance. In, timing-driven optimizations based on carry-save (CS) arithmetic were performed at the post-Register Transfer Level (RTL) design stage. In, common subexpression eliminationin CS computations is used to optimize linear DSP circuits. Verma et al. developed transformation techniques on theapplication’s DFG to maximize
  2. 2. A High-Performance FIR Filter Architecture for Fixed and Reconfigurable Applications the use of CS arithmetic prior theactual datapath synthesis. The aforementioned CS optimizationapproaches target inflexible datapath, i.e., ASIC, implementations. Recently, Xydis et al. proposed a flexible architecturecombining the ILP and pipelining techniques with the CS- awareoperation chaining. However, the entire aforementioned solutions featurean inherent limitation, i.e., CS optimization is bounded to mergingonly additions/subtractions. A CS to binary conversion is insertedbefore each operation that differs from addition/subtraction, e.g.,multiplication, thus, allocating multiple CS to binary conversionsthat heavily degrades performance due to time-consuming carrypropagations. Disadvantages:  high the area  high the power Proposed system: The proposed flexible accelerator architecture is shown in Fig. 1.Each FCU operates directly on CS operands and produces data inthe same form1 for direct reuse of intermediate results. Each FCU operates on 16-bit operands. Such a bit-length is adequate for themost DSP datapaths, but the architectural concept of the FCUcan be straightforwardly adapted for smaller or larger bit- lengths.The number of FCUs is determined at design time based on theILP and area constraints imposed by the designer. The CStoBinmodule is a ripple-carry adder and converts the CS form
  3. 3. A High-Performance FIR Filter Architecture for Fixed and Reconfigurable Applications to the two’scomplement one. The register bank consists of scratch registers andis used for storing intermediate results and sharing operands amongthe FCUs. Different DSP kernels (i.e., different register allocationand data communication patterns per kernel) can be mapped ontothe proposed architecture using post-RTL datapath interconnectionsharing techniques. The control unit drives the overallarchitecture (i.e., communication between the data port and theregister bank, configuration words of the FCUs and selection signalsfor the multiplexers) in each clock cycle. Structure of the Proposed Flexible Computational Unit: The structure of the FCU (Fig. 2) has been designed to enablehigh-performance flexible operation chaining based on a library of operation templates. Each FCU can be configured to anyof the T1–T5 operation templates shown in Fig. 3. Figure 1 : Abstract form of the flexible datapath. The proposedFCU enables intra-template operation chaining by fusing the additionsperformed before/after the multiplication and performs any partialoperation template of the following complex operations: W∗ = A × (X∗ + Y∗) + K∗ (1) W∗ = A × K∗ + (X∗ + Y ∗). (2)
  4. 4. A High-Performance FIR Filter Architecture for Fixed and Reconfigurable Applications Figure 2 : FCU. The following relation holds for all CS data: X∗ = {XC, XS} =XC + XS. The operand A is a two’s complement number. Thealternative execution paths in each FCU are specified after properlysetting the control signals of the multiplexers MUX1 and MUX2 (Fig. 2). The multiplexer MUX0 outputs Y ∗ when CL0 = 0(i.e., X∗ + Y ∗ is carried out) or Y ∗ when X∗ − Y ∗ is requiredand CL0 = 1. The two’s complement 4:2 CS adder produces theN∗ = X∗ +Y ∗ when the input carry equals 0 or the N∗ = X∗ −Y ∗when the input carry equals 1. The MUX1 determines if N∗ (1) orK∗ (2) is multiplied with A. TheMUX2 specifies if K∗ (1) or N∗ (2)is added with the multiplication product. The multiplexer MUX3accepts the output of MUX2 and its 1’s complement and outputsthe former one when an addition with the multiplication product isrequired (i.e., CL3 = 0) or the later one when a subtraction is carriedout (i.e., CL3 = 1). The 1- bit ace for the subtraction is added in theCS adder tree.
  5. 5. A High-Performance FIR Filter Architecture for Fixed and Reconfigurable Applications Figure 3 : FCU template library. The multiplier comprises a CS-to-MB module, which adopts arecently proposed techniqueto recode the 17-bit P∗ in itsrespective MB digits with minimal carry propagation. The multiplier’sproduct consists of 17 bits. The multiplier includes a compensationmethod for reducing the error imposed at the product’s accuracy bythe truncation technique. However, since all the FCU inputsconsist of 16 bits and provided that there are no overflows, the16 most significant bits of the 17-bit W∗ (i.e., the output of theCarry-Save Adder (CSA) tree, and thus, of the FCU) are inserted inthe appropriate FCU when requested. Advantages:  high degrees of computational density  reduce the area  reduce the power Software implementation:  Modelsim  Xilinx ISE

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