Work-in-Progress: DORY: Lightweight Memory Hierarchy Management for Deep NN Inference on IoT Endnodes

Abstract

IoT endnodes often couple a small and fast L1 scratchpad memory with higher-capacity but lower bandwidth and speed L2 background memory. The absence of a coherent hardware cache hierarchy saves energy but comes at the cost of labor-intensive explicit memory management, complicating the deployment of algorithms with large data memory footprint, such as Deep Neural Network (DNN) inference. In this work, we present DORY, a lightweight software-cache dedicated to DNN Deployment Oriented to memoRY. DORY leverages static data tiling and DMA-based double buffering to hide the complexity of manual L1-L2 memory traffic management. DORY enables storage of activations and weights in L2 with less than 4% performance overhead with respect to direct execution in L1. We show that a 142 kB DNN achieving 79.9% on CIFAR-10 runs 3.2x faster compared to its execution directly from L2 memory while consuming 1.9x less energy.