Logic Synthesis of Approximate Circuits

摘要

The ability of several important application domains to tolerate inexactness or approximations in a large fraction of their computations has lead to the advent of approximate computing, a new design paradigm that exploits the intrinsic error-resilient nature to optimize computing platforms for energy and performance. A promising approach to approximate computing is to design approximate circuits, or circuit implementations that are highly efficient but differ in functionality from their original specifications subject to a prespecified quality constraint. While a slew of manual design techniques for approximate circuits have demonstrated their significant potential, a key requirement for their mainstream adoption is to develop automatic methodologies and tools that are general and scalable to any given circuit and quality specification. In this article, we propose SALSA, a systematic methodology for automatic logic synthesis of approximate circuits. Given a golden RTL specification of a circuit and a quality constraint that defines the amount of error that may be introduced in the implementation, SALSA synthesizes an approximate version of the circuit that adheres to the prespecified quality bounds. We make two key contributions: 1) the rigorous formulation of the problem of approximate logic synthesis (ALS), enabling the generation of circuits that is corrected by construction and 2) mapping the problem of approximate synthesis into an equivalent traditional logic synthesis problem, thereby allowing the capabilities of existing synthesis tools to be fully utilized for ALS. In order to achieve these benefits, SALSA forms a virtual quality constraint circuit (QCC) that encodes the quality constraints using logic functions called Q-functions. It then captures the flexibility that engendered by them as approximation don't cares (ADCs), which are used for circuit simplification using traditional don't care-based optimization techniques. We utilized SALSA to automatically synthesize approximate circuits ranging from arithmetic building blocks (adders, multipliers, and MAC) to entire datapaths (DCT, FIR, IIR, SAD, FFT Butterfly, and Euclidean distance), demonstrating scalability and significant improvements in area (1.1x to 1.85x for tight error constraints, and 1.2x to 4.75x for relaxed error constraints) and power (1.15x to 1.75x for tight error constraints, and 1.3x to 5.25x for relaxed error constraints).