IEEE Circuits and Systems Magazine - Q3 2023 - 36

I. Introduction
D
eep neural network (DNN)-based machine learning
(ML) methods have become the dominant
way to solve problems in the fields of computer
vision (CV), natural language processing (NLP), autonomous
driving, and robotics [1], [2], [3], [4], [5], [6], [7],
[8], [9], [10], [11]. The effectiveness of the DNN-based
methods leads to the proliferation of DNN models, from
AlexNet [12] in 2012 for object detection and image
classification to GPT-3 [7] in 2020 for natural language
processing. In the quest towards higher accuracy and
expanded capabilities, newer models often grow in size
and require more memory and computation complexity.
Fig. 1 presents the accuracy of modern network models
along with their model size and complexity in terms of
number of parameters and operation counts. The evolution
of these models are shown in Fig. 2.
The widespread use of DNNs has made DNN computation
a workload class of itself. General-purpose graphics
processing units (GPUs) and central processing units
(CPUs) equipped with large compute parallelism and
memory bandwidth are popular hardware platforms for
accelerating DNN workloads in servers and clouds, but
GPUs and CPUs are not the most suitable for edge use
cases due to their high cost and energy consumption.
To fill the void, designing domain-specific accelerators
Figure 1. Top-1 accuracy, size, and complexity of modern DNN models. Adapted from [9] ©2018 IEEE.
Jie-Fang Zhang was with the Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA. He is now
with Nividia Corporation, Santa Clara, CA 95051 USA (e-mail: jfzhang@umich.edu).
Zhengya Zhang is with the Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA (e-mail:
zhengya@umich.edu).
36
IEEE CIRCUITS AND SYSTEMS MAGAZINE
THIRD QUARTER 2023

IEEE Circuits and Systems Magazine - Q3 2023

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