Generalized Water-filling for Source-aware Energy-efficient SRAMs

TL;DR

This paper introduces a novel information-theoretic approach to optimize bit-line swings in SRAMs, significantly reducing energy consumption while maintaining error constraints, through convex optimization and greedy algorithms.

Contribution

It formulates convex optimization problems for non-uniform bit-line swing optimization and proposes greedy algorithms, advancing energy-efficient SRAM design.

Findings

Energy consumption reduced by half at 8-bit words

Energy savings increase to four times at 16-bit words

Optimal non-uniform swings outperform uniform approaches

Abstract

Conventional low-power static random access memories (SRAMs) reduce read energy by decreasing the bit-line voltage swings uniformly across the bit-line columns. This is because the read energy is proportional to the bit-line swings. On the other hand, bit-line swings are limited by the need to avoid decision errors especially in the most significant bits. We propose an information-theoretic approach to determine optimal non-uniform bit-line swings by formulating convex optimization problems. For a given constraint on mean squared error of retrieved words, we consider criteria to minimize energy (for low-power SRAMs), maximize speed (for high-speed SRAMs), and minimize energy-delay product. These optimization problems can be interpreted as classical water-filling, ground-flattening and water-filling, and sand-pouring and water-filling, respectively. By leveraging these interpretations, we also propose greedy algorithms to obtain optimized discrete swings. Numerical results show that energy-optimal swing assignment reduces energy consumption by half at a peak signal-to-noise ratio of 30dB for an 8-bit accessed word. The energy savings increase to four times for a 16-bit accessed word.