PUFFIN: An Efficient DNN Training Accelerator for Direct Feedback Alignment in FeFET

Abstract

The currently widely used backpropagation (BP) training algorithm requires that all trainable weights in a deep neural network (DNN) be stored in memory and used sequentially in the backward path, which makes training parallelization extremely challenging and also incurs significant memory and computing overheads. Although emerging ReRAM-based computing has demonstrated great potential for DNN acceleration, state-of-the-art designs suffer from >60% energy overhead for analog-to-digital converters (ADCs). In this work, we propose PUFFIN, an efficient DNN training accelerator for Direct Feedback Alignment (DFA). PUFFIN leverages DFA to overcome the limitation of long-range data dependency required by BP and executes an L -layer DNN training in parallel in an (L+2)-stage pipeline. We implement PUFFIN using Ferroelectric Field-Effect Transistors (FeFET) due to their high performance and low-power operation. To further improve the power efficiency, we propose a random number generator (RNG) based on the statistical switching in FeFET device and an ultra-low power FeFET-based ADC. Compared to previous ReRAM-based training accelerators, PUFFIN achieves 1.3 × speedup and 2.5× improvement on power efficiency.