A 28nm 1.644TFLOPS/W Floating-Point Computation SRAM Macro with Variable Precision for Deep Neural Network Inference and Training

Abstract

This paper presents a digital compute-in-memory (CIM) macro for accelerating deep neural networks. The macro provides high-precision computation required for training deep neural networks and running state-of-the-art models by supporting floating-point MAC operations. Additionally, the design supports variable computation precision, enabling optimized processing for different models and tasks. The design achieves 1.644TFLOPS/W energy efficiency and 57.9GFLOPS/mm2 computation density while supporting a wide range of floating-point data formats and computation precisions.