QUIDAM: A Framework for <u>Qu</u> ant <u>i</u> zation-aware <u>D</u> NN <u>A</u> ccelerator and <u>M</u> odel Co-Exploration
نویسندگان
چکیده
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, model compression techniques, there is a need for design space exploration frameworks that incorporate quantization-aware processing elements into accelerator while having accurate fast power, performance, area models. In this work, we present QUIDAM , highly parameterized DNN co-exploration framework. Our framework can facilitate future research on of accelerators various choices such as bit precision, element type, scratchpad sizes elements, global buffer size, number total configurations. results show different precisions types lead significant differences in terms performance per energy. Specifically, our identifies wide range points where varies more than 5× 35×, respectively. With proposed framework, lightweight par accuracy up 5.7× improvement when compared best 16-bit integer quantization–based implementation. Finally, due pre-characterized models, speed process by three four orders magnitude it removes expensive synthesis characterization each design.
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ژورنال
عنوان ژورنال: ACM Transactions in Embedded Computing Systems
سال: 2023
ISSN: ['1539-9087', '1558-3465']
DOI: https://doi.org/10.1145/3555807