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%2C an innovative quantization-aware training method that could fine-tune the per-weight and per-activation precision by making them optimizable through gradient descent. This approach enables ultra-low latency and low power neural networks on hardware capable of performing arithmetic operations with an arbitrary number of bits%2C such as FPGAs and ASICs. We demonstrate that HGQ can outperform existing methods by a substantial margin%2C achieving resource reduction by up to a factor of 20 and latency improvement by a factor of 5 while preserving accuracy.?quality=80&w=800)
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