SC-IMC: Algorithm-Architecture Co-Optimized SRAM-Based In-Memory Computing for Sine/Cosine and Convolutional Acceleration

Sine/cosine (SC) is widely used in practical engineering applications, such as image compression and motor control. Nevertheless, due to power sensitivity and speed demands, SC acceleration suffers from limitations in traditional von-Neumann architectures. To overcome this challenge, we propose accelerating SC and convolution using a static random access memory (SRAM)-based in-memory computing (IMC) architecture through an algorithm-architecture co-optimization manner. We develop the first SC algorithm that transforms nonlinear operations into the IMC paradigm, enabling IMC array to handle both SC and artificial intelligence (AI) tasks and making the IMC array a reusable module. Our architecture extends computing functions of macro dedicated to convolutional neural networks (CNNs), with less than a 1% area increase. The proposed SC algorithm for FP32 data achieves high accuracy within 1 unit in the least significant place (ulp) error margin compared with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</i> math library. Moreover, we build an intelligent IMC system that supports various CNNs. Our IMC macro implements 512-kb binary weight storage within 3.0366-mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> area in SMIC 28-nm technology and presents area/energy efficiency of 2160.29–270.04 GOPS/mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 513.95–8.03 TOPS/W in CNN mode. The proposed algorithm and architecture facilitate the integration of more nonlinear functions into IMC with minimal area overhead.