An Empirical Study of Low Precision Quantization for TinyML Post date March 24, 2022 ← Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images → Power-of-Two Quantization for Low Bitwidth and Hardware Compliant Neural Networks
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