“State-of-the-art Model Quantization and Optimization for Efficient Edge AI,” a Presentation from DEEPX

Hyunjin Kim, Senior Staff Engineer at DEEPX, presents the “State-of-the-art Model Quantization and Optimization for Efficient Edge AI” tutorial at the May 2023 Embedded Vision Summit.

Extremely efficient edge AI requires more than efficient processors; it also requires tools capable of generating superefficient software. In this talk, Kim explains and demonstrates 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.

Kim begins by describing how the DXNN SDK uses hardware-aware, selective quantization to maintain high accuracy while achieving efficient DNN implementations. Next, he explains how the SDK maps DNN layer operations into processor micro-operations to provide both efficiency and flexibility. Kim also shows how the DEEPX SDK conserves memory by utilizing tiling, layer fusion and feature reuse. Finally, he illustrates 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.

See here for a PDF of the slides.

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