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PRODID:-//Cornell U. Department of Computer Science//Brown Bag Seminar//EN
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SUMMARY:Brown bag: Zhiru Zhang
DESCRIPTION:Title: Algorithm-Accelerator Co-Design for Neural Network
	 Specialization\nSpeaker: Zhiru Zhang\nAbstract: In recent years\,
	 machine learning (ML) with deep neural networks (DNNs) has been widely
	 deployed in diverse application domains. However\, the growing
	 complexity of DNN models\, the slowdown of technology scaling\, and the
	 proliferation of edge devices are driving a demand for higher DNN
	 performance and energy efficiency. ML applications have shifted from
	 general-purpose processors to dedicated hardware accelerators in both
	 academic and commercial settings. In line with this trend\, there has
	 been an active body of research on both algorithms and hardware
	 architectures for neural network specialization.\n\nThis talk presents
	 our recent investigation into DNN optimization and low-precision
	 quantization\, using a co-design approach featuring contributions to
	 both algorithms and hardware accelerators. First\, we review static
	 network pruning techniques and show a fundamental link between group
	 convolutions and circulant matrices -- two previously disparate lines of
	 research in DNN compression. Then we discuss channel gating\, a
	 dynamic\, fine-grained\, and trainable technique for DNN acceleration.
	 Unlike static approaches\, channel gating exploits input-dependent
	 dynamic sparsity at run time. This results in a significant reduction in
	 compute cost with a minimal impact on accuracy. Finally\, we present
	 outlier channel splitting\, a technique to improve DNN weight
	 quantization by removing outliers from the weight distribution without
	 retraining.
LOCATION:Gates 122
UID:2019-09-10
STATUS:TENTATIVE
DTSTART:20190910T160000Z
DTEND:20190910T170000Z
LAST-MODIFIED:20190904T141743Z
ORGANIZER;CN=Jonathan Shi:http://www.cs.cornell.edu/~jshi/brownbag/
DTSTAMP:20260408T121836Z
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