TinyFoA: Memory Efficient Forward-Only Algorithm for On-Device Learning

Abstract

Forward-only algorithms offer a promising memory-efficient alternative to Backpropagation (BP) for on-device learning. However, state-of-the-art forward-only algorithms, e.g., Forward-Forward (FF), still require a substantial amount of memory during the training process, often exceeding the limits of mobile edge and Internet of Things (IoT) devices. At the same time, existing memory-optimization techniques, e.g., binarizing parameters and activations, are mainly designed for BP, hence significantly degrading the classification performance when applied to state-of-the-art forward-only algorithms. In this paper, we propose a memory-efficient forward-only algorithm called TinyFoA, to reduce dynamic memory overhead in the training process. Our TinyFoA optimizes the memory efficiency not only by layer-wise training but also by partially updating each layer, as well as by binarizing the weights and the activations. We extensively evaluate our proposed TinyFoA against BP and other forward-only algorithms and demonstrate its effectiveness and superiority compared to state-of-the-art forward-only algorithms in terms of classification performance and training memory overhead, reducing the memory overheads by an order of magnitude.