SEA: Sign-Separated Accumulation Scheme for Resource-Efficient DNN Accelerators

Deep neural network (DNN) accelerators targeting training need to support the resource-hungry floating-point (FP) arithmetic. Typically, the additions (accumulations) are performed with higher precision compared to multiplications, resulting in a substantial proportion of resources consumed by the adders. In this paper, we aim to improve the resource efficiency of FP addition in DNN training accelerators and present a sign-separated accumulation scheme (SEA). The proposed SEA scheme accumulates the same-signed terms separately, followed by a final addition of the two oppositely-signed sub-accumulations. The separate sub-accumulations, when performed using our resource-efficient same-signed FP adders, result in significant improvement in overall resource efficiency. We present a SEA-based systolic array design, involving a novel processing element design, capable of performing FP matrix multiplications for DNN training. Our experimental results show substantial improvements in delay, area-delay product (ADP), and energy consumption, by achieving reductions of up to 19.8% in delay, 29.1% in ADP and 30.7% in energy, across various adder-multiplier combinations compared to the original designs. SEA introduces no approximations in accumulation or modifications in the rounding mechanism. We integrate the SEA-based systolic array into the open-source Gemmini [1] ecosystem for use by the broader community.