English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

As the machine learning and systems community strives to achieve higherenergy-efficiency through custom DNN accelerators and model compressiontechniques, there is a need for a design space exploration framework thatincorporates quantization-aware processing elements into the accelerator designspace while having accurate and fast power, performance, and area models. Inthis work, we present QAPPA, a highly parameterized quantization-aware power,performance, and area modeling framework for DNN accelerators. Our frameworkcan facilitate the future research on design space exploration of DNNaccelerators for various design choices such as bit precision, processingelement type, scratchpad sizes of processing elements, global buffer size,device bandwidth, number of total processing elements in the the design, andDNN workloads. Our results show that different bit precisions and processingelement types lead to significant differences in terms of performance per areaand energy. Specifically, our proposed lightweight processing elements achieveup to 4.9x more performance per area and energy improvement when compared toINT16 based implementation.

QAPPA: Quantization-Aware Power, Performance, and Area Modeling of DNN Accelerators