A heterogeneous microprocessor for energy-scalable sensor inference using genetic programming

Hongyang Jia; Jie Lu; Niraj K. Jha; Naveen Yerma

doi:10.23919/VLSIC.2017.8008535

A heterogeneous microprocessor for energy-scalable sensor inference using genetic programming

Hongyang Jia, Jie Lu, Niraj K. Jha, Naveen Yerma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

Abstract

We present a heterogeneous microprocessor for IoE sensor-inference applications, which achieves programmability required for feature extraction strictly using application data. Acceleration, though key for energy efficiency, poses substantial programmability challenges. These are overcome by exploiting genetic programming (GP) for automatic program synthesis. GP yields highly structured models of computation, enabling: (1) high degree of specialization; (2) systematic mapping of programs to the accelerator; and (3) energy scalability via user-controllable approximation. The microprocessor (130nm) achieves 325×/156× energy reduction, and farther 20x/9x energy scalability, for programmable feature extraction in two medical-sensor applications (seizure/arrhythmia-detection) vs. GP-model execution on CPU. The energy efficiency is 220 GOPS/W, near that of fixed-function accelerators, exceeding typical programmable accelerators.

Original language	English (US)
Title of host publication	2017 Symposium on VLSI Circuits, VLSI Circuits 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	C28-C29
ISBN (Electronic)	9784863486065
DOIs	https://doi.org/10.23919/VLSIC.2017.8008535
State	Published - Aug 10 2017
Event	31st Symposium on VLSI Circuits, VLSI Circuits 2017 - Kyoto, Japan Duration: Jun 5 2017 → Jun 8 2017

Publication series

Name	IEEE Symposium on VLSI Circuits, Digest of Technical Papers

Other

Other	31st Symposium on VLSI Circuits, VLSI Circuits 2017
Country/Territory	Japan
City	Kyoto
Period	6/5/17 → 6/8/17

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

Access to Document

10.23919/VLSIC.2017.8008535

Cite this

Jia, H., Lu, J., Jha, N. K., & Yerma, N. (2017). A heterogeneous microprocessor for energy-scalable sensor inference using genetic programming. In 2017 Symposium on VLSI Circuits, VLSI Circuits 2017 (pp. C28-C29). Article 8008535 (IEEE Symposium on VLSI Circuits, Digest of Technical Papers). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/VLSIC.2017.8008535

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title = "A heterogeneous microprocessor for energy-scalable sensor inference using genetic programming",

abstract = "We present a heterogeneous microprocessor for IoE sensor-inference applications, which achieves programmability required for feature extraction strictly using application data. Acceleration, though key for energy efficiency, poses substantial programmability challenges. These are overcome by exploiting genetic programming (GP) for automatic program synthesis. GP yields highly structured models of computation, enabling: (1) high degree of specialization; (2) systematic mapping of programs to the accelerator; and (3) energy scalability via user-controllable approximation. The microprocessor (130nm) achieves 325×/156× energy reduction, and farther 20x/9x energy scalability, for programmable feature extraction in two medical-sensor applications (seizure/arrhythmia-detection) vs. GP-model execution on CPU. The energy efficiency is 220 GOPS/W, near that of fixed-function accelerators, exceeding typical programmable accelerators.",

author = "Hongyang Jia and Jie Lu and Jha, {Niraj K.} and Naveen Yerma",

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series = "IEEE Symposium on VLSI Circuits, Digest of Technical Papers",

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Jia, H, Lu, J, Jha, NK & Yerma, N 2017, A heterogeneous microprocessor for energy-scalable sensor inference using genetic programming. in 2017 Symposium on VLSI Circuits, VLSI Circuits 2017., 8008535, IEEE Symposium on VLSI Circuits, Digest of Technical Papers, Institute of Electrical and Electronics Engineers Inc., pp. C28-C29, 31st Symposium on VLSI Circuits, VLSI Circuits 2017, Kyoto, Japan, 6/5/17. https://doi.org/10.23919/VLSIC.2017.8008535

A heterogeneous microprocessor for energy-scalable sensor inference using genetic programming. / Jia, Hongyang; Lu, Jie; Jha, Niraj K. et al.
2017 Symposium on VLSI Circuits, VLSI Circuits 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. C28-C29 8008535 (IEEE Symposium on VLSI Circuits, Digest of Technical Papers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - We present a heterogeneous microprocessor for IoE sensor-inference applications, which achieves programmability required for feature extraction strictly using application data. Acceleration, though key for energy efficiency, poses substantial programmability challenges. These are overcome by exploiting genetic programming (GP) for automatic program synthesis. GP yields highly structured models of computation, enabling: (1) high degree of specialization; (2) systematic mapping of programs to the accelerator; and (3) energy scalability via user-controllable approximation. The microprocessor (130nm) achieves 325×/156× energy reduction, and farther 20x/9x energy scalability, for programmable feature extraction in two medical-sensor applications (seizure/arrhythmia-detection) vs. GP-model execution on CPU. The energy efficiency is 220 GOPS/W, near that of fixed-function accelerators, exceeding typical programmable accelerators.

AB - We present a heterogeneous microprocessor for IoE sensor-inference applications, which achieves programmability required for feature extraction strictly using application data. Acceleration, though key for energy efficiency, poses substantial programmability challenges. These are overcome by exploiting genetic programming (GP) for automatic program synthesis. GP yields highly structured models of computation, enabling: (1) high degree of specialization; (2) systematic mapping of programs to the accelerator; and (3) energy scalability via user-controllable approximation. The microprocessor (130nm) achieves 325×/156× energy reduction, and farther 20x/9x energy scalability, for programmable feature extraction in two medical-sensor applications (seizure/arrhythmia-detection) vs. GP-model execution on CPU. The energy efficiency is 220 GOPS/W, near that of fixed-function accelerators, exceeding typical programmable accelerators.

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A heterogeneous microprocessor for energy-scalable sensor inference using genetic programming

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