INVITED: Efficient Synthesis of Compact Deep Neural Networks
INVITED: Efficient Synthesis of Compact Deep Neural Networks
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DOI：
10.1109/dac18072.2020.9218529
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发表时间：
2020-04
期刊：
影响因子：
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通讯作者：
Wenhan Xia;Hongxu Yin;N. Jha
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文献类型：
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作者：
Wenhan Xia;Hongxu Yin;N. Jha
深度神经网络 (DNN) 已部署在无数机器学习应用中。然而，其准确性的提高通常是通过日益复杂和深入的网络架构来实现的。这些大型、深度模型通常不适合现实世界的应用，因为它们的计算成本巨大、内存带宽高和延迟长。例如，自动驾驶需要基于在运行时能源和内存存储限制下运行的物联网 (IoT) 边缘设备进行快速推理。在这种情况下，紧凑型 DNN 可以降低能耗、内存需求和推理延迟，从而促进部署。长短期记忆 (LSTM) 是一种循环神经网络，在顺序数据建模中也得到广泛应用。他们还面临模型大小与准确性的权衡。在本文中，我们回顾了自动合成适合实际应用的紧凑而准确的 DNN/LSTM 模型的主要方法。我们还概述了一些挑战和未来的探索领域。
Deep neural networks (DNNs) have been deployed in myriad machine learning applications. However, advances in their accuracy are often achieved with increasingly complex and deep network architectures. These large, deep models are often unsuitable for real-world applications, due to their massive computational cost, high memory bandwidth, and long latency. For example, autonomous driving requires fast inference based on Internet-of-Things (IoT) edge devices operating under run-time energy and memory storage constraints. In such cases, compact DNNs can facilitate deployment due to their reduced energy consumption, memory requirement, and inference latency. Long short-term memories (LSTMs) are a type of recurrent neural network that have also found widespread use in the context of sequential data modeling. They also face a model size vs. accuracy trade-off. In this paper, we review major approaches for automatically synthesizing compact, yet accurate, DNN/LSTM models suitable for real-world applications. We also outline some challenges and future areas of exploration.