Abstract:Quantized neural networks are well known for reducing latency, power consumption, and model size without significant degradation in accuracy, making them highly applicable for systems with limited resources and low power requirements. Mixed precision quantization offers better utilization of customized hardware that supports arithmetic operations at different bitwidths. Existing mixed-precision schemes rely on having a high exploration space, resulting in a large carbon footprint. In addition, these bit alloca… Show more
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