Extremely efficient edge AI requires more than efficient processors; it also requires tools capable of generating superefficient software. In this talk, we’ll explain and demonstrate how DEEPX’s DXNN SDK utilizes state-of-the-art optimization techniques to generate extremely efficient, accurate code for DEEPX’s new M1 neural processor. We’ll begin by describing how the DXNN SDK uses hardware-aware, selective quantization to maintain high accuracy while achieving efficient DNN implementations. Next, we’ll explain how the SDK maps DNN layer operations into processor micro-operations to provide both efficiency and flexibility. We’ll also show how the DEEPX SDK conserves memory by utilizing tiling, layer fusion and feature reuse. Finally, we’ll illustrate the ease of use of the SDK by demonstrating the use of the DXNN SDK to implement a state-of-the-art model on the M1 NPU.