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

Hongyang Jia; Jie Lu; Niraj Kumar 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 Kumar Jha, Naveen Yerma

Electrical and Computer Engineering

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

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

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10.23919/VLSIC.2017.8008535

Cite this

@inproceedings{0b11a783ec104eddae74fdc652e9567b,

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 Kumar} and Naveen Yerma",

year = "2017",

month = aug,

day = "10",

doi = "10.23919/VLSIC.2017.8008535",

language = "English (US)",

pages = "C28--C29",

booktitle = "2017 Symposium on VLSI Circuits, VLSI Circuits 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "31st Symposium on VLSI Circuits, VLSI Circuits 2017 ; Conference date: 05-06-2017 Through 08-06-2017",

}

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, 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

TY - GEN

T1 - A heterogeneous microprocessor for energy-scalable sensor inference using genetic programming

AU - Jia, Hongyang

AU - Lu, Jie

AU - Jha, Niraj Kumar

AU - Yerma, Naveen

PY - 2017/8/10

Y1 - 2017/8/10

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.

UR - http://www.scopus.com/inward/record.url?scp=85034044499&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85034044499&partnerID=8YFLogxK

U2 - 10.23919/VLSIC.2017.8008535

DO - 10.23919/VLSIC.2017.8008535

M3 - Conference contribution

AN - SCOPUS:85034044499

SP - C28-C29

BT - 2017 Symposium on VLSI Circuits, VLSI Circuits 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 31st Symposium on VLSI Circuits, VLSI Circuits 2017

Y2 - 5 June 2017 through 8 June 2017

ER -

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

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