  As the machine learning and systems communities strive to achieve higher
energy-efficiency through custom deep neural network (DNN) accelerators, varied
bit precision or quantization levels, there is a need for design space
exploration frameworks that incorporate quantization-aware processing elements
(PE) into the accelerator design space while having accurate and fast power,
performance, and area models. In this work, we present QADAM, a highly
parameterized quantization-aware power, performance, and area modeling
framework for DNN accelerators. Our framework can facilitate future research on
design space exploration and Pareto-efficiency of DNN accelerators for various
design choices such as bit precision, PE type, scratchpad sizes of PEs, global
buffer size, number of total PEs, and DNN configurations. Our results show that
different bit precisions and PE types lead to significant differences in terms
of performance per area and energy. Specifically, our framework identifies a
wide range of design points where performance per area and energy varies more
than 5x and 35x, respectively. We also show that the proposed lightweight
processing elements (LightPEs) consistently achieve Pareto-optimal results in
terms of accuracy and hardware-efficiency. With the proposed framework, we show
that LightPEs achieve on par accuracy results and up to 5.7x more performance
per area and energy improvement when compared to the best INT16 based design.
