No abstract

Approximate arithmetic has recently emerged as a promising paradigm for many imprecision-tolerant applications. It can offer substantial reductions in circuit complexity, delay and energy consumption by relaxing accuracy requirements. In this paper, we propose a novel energy-efficient approximate multiplier design using a significance-driven logic compression (SDLC) approach. Fundamental to this approach is an algorithmic and configurable lossy compression of the partial product rows based on their progressive bit significance. This is followed by the commutative remapping of the resulting product terms to reduce the number of product rows. As such, the complexity of the multiplier in terms of logic cell counts and lengths of critical paths is drastically reduced. A number of multipliers with different bit-widths (4-bit to 128-bit) are designed in SystemVerilog and synthesized using Synopsys Design Compiler. Post-synthesis experiments showed that up to an order of magnitude energy savings, and reductions of 65% in critical delay and almost 45% in silicon area can be achieved for a 128-bit multiplier compared to an accurate equivalent. These gains are achieved with low accuracy losses estimated at less than 0.00071 mean relative error. Additionally, we demonstrate the energy-accuracy trade-offs for different degrees of compression, achieved through configurable logic clustering. In evaluating the effectiveness of our approach, a case study image processing application showed up to 68.3% energy reduction with negligible losses in image quality expressed as peak signal-to-noise ratio (PSNR).

Abstract. Dual-rail encoding, return-to-spacer protocol and hazard-free logic can be used to resist differential power analysis attacks by making the power consumption independent of processed data. Standard dual-rail logic uses a protocol with a single spacer, e.g. all-zeroes, which gives rise to power balancing problems. We address these problems by incorporating two spacers; the spacers alternate between adjacent clock cycles. This guarantees that all gates switch in each clock cycle regardless of the transmitted data values. To generate these dual-rail circuits an automated tool has been developed. It is capable of converting synchronous netlists into dual-rail circuits and it is interfaced to industry CAD tools. Dual-rail and single-rail benchmarks based upon the Advanced Encryption Standard (AES) have been simulated and compared in order to evaluate the method.

Designing asynchronous circuits by reusing existing synchronous tools has become a promising solution to the problem of poor CAD support in asynchronous world. A straightforward way is to structurally map the gates in a synchronous netlist to their functionally equivalent modules which use delay-insensitive codes. Different trade-offs exist in previous methods between the overheads of the implementations and their robustness. The aim of this paper is to optimise the area of asynchronous circuits using partial acknowledgement concept. We employ this concept in two design flows, which are implemented in a software tool to evaluate the efficiency of the method. The benchmark results show the average reduction in area by 28% and in the number of inter-functional module wires that require timing verification by 67%, compared to NCL-X.

Approximate arithmetic has recently emerged as a promising paradigm for many imprecision-tolerant applications. It can offer substantial reductions in circuit complexity, delay and energy consumption by relaxing accuracy requirements. In this paper, we propose a novel energy-efficient approximate multiplier design using a significance-driven logic compression (SDLC) approach. Fundamental to this approach is an algorithmic and configurable lossy compression of the partial product rows based on their progressive bit significance. This is followed by the commutative remapping of the resulting product terms to reduce the number of product rows. As such, the complexity of the multiplier in terms of logic cell counts and lengths of critical paths is drastically reduced. A number of multipliers with different bit-widths (4-bit to 128-bit) are designed in SystemVerilog and synthesized using Synopsys Design Compiler. Post-synthesis experiments showed that up to an order of magnitude energy savings, and reductions of 65% in critical delay and almost 45% in silicon area can be achieved for an 128-bit multiplier, compared to an accurate equivalent. These gains are achieved with low accuracy losses estimated at less than 0.0028 mean relative error. Additionally, we demonstrate the performance-energyquality (PEQ) trade-offs for different degrees of compression, achieved through configurable logic clustering. While evaluating the effectiveness of the proposed approach three case studies were set up. First, a Gaussian blur filter was designed, which demonstrated up to 80% energy reduction with a meagre loss of image quality. Second, we evaluate our approach in machine learning application using perceptron classifier, showed up to 74% energy reduction with negligible error rate. Third, the proposed multiplier designs were used in a power-constrained image processing application. We showed that SDLC can achieve 60x improvement in computation capability, with potential to be employed in ubiquitous systems.