Approximate Logic Synthesis: A Survey

Abstract

Approximate computing is an emerging paradigm that, by relaxing the requirement for full accuracy, offers benefits in terms of design area and power consumption. This paradigm is particularly attractive in applications where the underlying computation has inherent resilience to small errors. Such applications are abundant in many domains, including machine learning, computer vision and signal processing. In circuit design, a major challenge is the capability to synthesize approximate circuits automatically, without manually relying on the expertise of designers. In this work, we review methods devised to synthesize approximate circuits given their exact functionality and an approximability threshold. We summarize strategies for evaluating the error that circuit simplification can induce on the output, which guide synthesis techniques in choosing the circuit transformations that lead to the largest benefit for a given amount of induced error. We then review circuit simplification methods that operate at gate or Boolean level, including those that leverage classical Boolean synthesis techniques to realize the approximations. We also summarize strategies that take highlevel descriptions such as C or behavioral Verilog and synthesize approximate circuits from these descriptions.

Evolutionary Algorithms in Approximate Computing: A Survey

“…In the basic case, it takes a form of a suitable bit width reduction or pruning of less critical parts of the circuit. However, sophisticated strategies based on logic resynthesis have also been proposed [4]. Approximations can be injected to the memory subsystem by approximating the cache functionality, memory operations, or elementary memory cells.…”

“…However, a recent trend is to develop fully automated approximation methods capable of (i) identifying suitable components that should undergo the approximation process with the highest priority, (ii) generating candidate approximate implementations, and (iii) evaluating them concerning various criteria. These methods are constructed using iterative heuristics [4].…”

“…Many design automation methods have been developed to create approximate implementations for this important class of applications [2,3,4]. State-of-the-art results were quite often obtained by evolutionary algorithms (EAs) [5].…”

Fast Logic Optimization Using Decision Trees

“…It may also enable improvements on classic logic optimizations, as in [13], where learning models are used to detect circuit locations to be optimized. ML techniques also provide the ability to synthesize and optimize approximate logic circuits [14] for error resilient applications.…”

FPCAM: Floating Point Configurable Approximate Multiplier for Error Resilient Applications

“…Approximate circuits have been gaining a lot of popularity in recent years [1], [2]. Approximate circuits exploit the application's resilience towards the introduction of approximation to provide benefits in power, area, and delay [3]- [5].…”