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14.7 A 288µW programmable deep-learning processor with 270KB on-chip weight storage using non-uniform memory hierarchy for mobile intelligence | IEEE Conference Publication | IEEE Xplore
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14.7 A 288µW programmable deep-learning processor with 270KB on-chip weight storage using non-uniform memory hierarchy for mobile intelligence


Abstract:

Deep learning has proven to be a powerful tool for a wide range of applications, such as speech recognition and object detection, among others. Recently there has been in...Show More

Abstract:

Deep learning has proven to be a powerful tool for a wide range of applications, such as speech recognition and object detection, among others. Recently there has been increased interest in deep learning for mobile IoT [1] to enable intelligence at the edge and shield the cloud from a deluge of data by only forwarding meaningful events. This hierarchical intelligence thereby enhances radio bandwidth and power efficiency by trading-off computation and communication at edge devices. Since many mobile applications are “always-on” (e.g., voice commands), low power is a critical design constraint. However, prior works have focused on high performance reconfigurable processors [2-3] optimized for large-scale deep neural networks (DNNs) that consume >50mW. Off-chip weight storage in DRAM is also common in the prior works [2-3], which implies significant additional power consumption due to intensive off-chip data movement.
Date of Conference: 05-09 February 2017
Date Added to IEEE Xplore: 06 March 2017
ISBN Information:
Electronic ISSN: 2376-8606
Conference Location: San Francisco, CA, USA

References

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