  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. In
this work, we present QAPPA, a highly parameterized quantization-aware power,
performance, and area modeling framework for DNN accelerators. Our framework
can facilitate the future research on design space exploration of DNN
accelerators for various design choices such as bit precision, processing
element type, scratchpad sizes of processing elements, global buffer size,
device bandwidth, number of total processing elements in the the design, and
DNN workloads. Our results show that different bit precisions and processing
element types lead to significant differences in terms of performance per area
and energy. Specifically, our proposed lightweight processing elements achieve
up to 4.9x more performance per area and energy improvement when compared to
INT16 based implementation.
