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QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration
As the machine learning and systems communities strive to achieve higher energy-efficiency through custom deep neural network (DNN) accelerators, varied precision or quantization levels, and model compression techniques, there is a need for design space exploration frameworks that incorporate quantization-aware processing elements into the accelerator design space while having accurate and fast power, performance, and area models.
QADAM: Quantization-Aware DNN Accelerator Modeling for Pareto-Optimality
We also show that the proposed lightweight processing elements (LightPEs) consistently achieve Pareto-optimal results in terms of accuracy and hardware-efficiency.
QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators
As the machine learning and systems community strives to achieve higher energy-efficiency through custom DNN accelerators and model compression techniques, there is a need for a design space exploration framework that incorporates quantization-aware processing elements into the accelerator design space while having accurate and fast power, performance, and area models.
