JPEG-ACT: Accelerating Deep Learning via Transform-based Lossy Compression

A reduction in the time it takes to train machine learning models can be translated into improvements in accuracy. An important factor that increases training time in deep neural networks (DNNs) is the need to store large amounts of temporary data during the back-propagation algorithm. To enable tra...

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Vydané v:2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA) s. 860 - 873
Hlavní autori: Evans, R. David, Liu, Lufei, Aamodt, Tor M.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.05.2020
Predmet:
Accuracy
CNN Training
Compression
Computer architecture
Convolutional neural networks
Deep learning
GPU
Graphics processing units
Hardware acceleration
Image coding
Machine learning algorithms
Training
Transform coding
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Popis
Shrnutí:A reduction in the time it takes to train machine learning models can be translated into improvements in accuracy. An important factor that increases training time in deep neural networks (DNNs) is the need to store large amounts of temporary data during the back-propagation algorithm. To enable training very large models this temporary data can be offloaded from limited size GPU memory to CPU memory but this data movement incurs large performance overheads.We observe that in one important class of DNNs, convolutional neural networks (CNNs), there is spatial correlation in these temporary values. We propose JPEG for ACTivations (JPEGACT), a lossy activation offload accelerator for training CNNs that works by discarding redundant spatial information. JPEGACT adapts the well-known JPEG algorithm from 2D image compression to activation compression. We show how to optimize the JPEG algorithm so as to ensure convergence and maintain accuracy during training. JPEG-ACT achieves 2.4\times higher training performance compared to prior offload accelerators, and 1.6\times compared to prior activation compression methods. An efficient hardware implementation allows JPEG-ACT to consume less than 1% of the power and area of a modern GPU.
DOI:10.1109/ISCA45697.2020.00075