Exploration of Design Space and Runtime Optimization for Affective Computing in Machine Learning Empowered Ultra-Low Power SoC

Machine Learning Oriented Resource Allocation to Achieve Ultra Low Power, Low Latency and High Reliability Vehicular Communication Networks

2020 IEEE 17th India Council International Conference (INDICON) ◽

10.1109/indicon49873.2020.9342584 ◽

2020 ◽

Author(s):

Sasweth C Rajanarayanan ◽

Rohit Misra ◽

Rahul Jashvantbhai Pandya

Keyword(s):

Machine Learning ◽

Resource Allocation ◽

Low Power ◽

Communication Networks ◽

High Reliability ◽

Low Latency ◽

Vehicular Communication ◽

Ultra Low Power ◽

Vehicular Communication Networks

2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP) ◽

Design Space Exploration for Orlando Ultra Low-Power Convolutional Neural Network SoC

10.1109/asap.2018.8445096 ◽

2018 ◽

Author(s):

Ahmet Erdem ◽

Cristina Silvano ◽

Thomas Boesch ◽

Andrea Ornstein ◽

Surinder-Pal Singh ◽

...

Keyword(s):

Neural Network ◽

Low Power ◽

Convolutional Neural Network ◽

Design Space ◽

Space Exploration ◽

Architecture of Computing Systems – ARCS 2012 - Lecture Notes in Computer Science ◽

Design Space Exploration of Hybrid Ultra Low Power Branch Predictors

10.1007/978-3-642-28293-5_16 ◽

2012 ◽

pp. 184-199 ◽

Cited By ~ 1

Author(s):

Matthew Bielby ◽

Miles Gould ◽

Nigel Topham

Keyword(s):

Low Power ◽

Design Space ◽

Space Exploration ◽

From algorithms to devices: Enabling machine learning through ultra-low-power VLSI mixed-signal array processing

2017 IEEE Custom Integrated Circuits Conference (CICC) ◽

10.1109/cicc.2017.7993650 ◽

2017 ◽

Cited By ~ 4

Author(s):

Siddharth Joshi ◽

Chul Kim ◽

Sohmyung Ha ◽

Gert Cauwenberghs

Keyword(s):

Machine Learning ◽

Low Power ◽

Array Processing ◽

Ultra Low Power ◽

Mixed Signal

Towards on-node Machine Learning for Ultra-low-power Sensors Using Asynchronous Σ Δ Streams

ACM Journal on Emerging Technologies in Computing Systems ◽

10.1145/3404975 ◽

2020 ◽

Vol 16 (4) ◽

pp. 1-20

Author(s):

Patricia Gonzalez-Guerrero ◽

Tommy Tracy II ◽

Xinfei Guo ◽

Rahul Sreekumar ◽

Marzieh Lenjani ◽

...

Keyword(s):

Machine Learning ◽

Low Power ◽

Runtime Design Space Exploration and Mapping of DCNNs for the Ultra-Low-Power Orlando SoC

ACM Transactions on Architecture and Code Optimization ◽

10.1145/3379933 ◽

2020 ◽

Vol 17 (2) ◽

pp. 1-25

Author(s):

Ahmet Erdem ◽

Cristina Silvano ◽

Thomas Boesch ◽

Andrea Carlo Ornstein ◽

Surinder-Pal Singh ◽

...

Keyword(s):

Low Power ◽

Design Space ◽

Space Exploration ◽

SSCS-Singapore Holds Distinguished Lecture on Enabling and Exploiting: Machine Learning in Ultra-Low-Power Devices by Prof. Naveen Verma [Chapters]

IEEE Solid-State Circuits Magazine ◽

10.1109/mssc.2017.2746170 ◽

2017 ◽

Vol 9 (4) ◽

pp. 112-113

Author(s):

Jiang Wenyu

Keyword(s):

Machine Learning ◽

Low Power ◽

Power Devices ◽

Ultra Low Power ◽

Ultra Low Power Devices

NCS based ultra low power optimized machine learning techniques for image classification

2019 IEEE Region 10 Symposium (TENSYMP) ◽

10.1109/tensymp46218.2019.8971238 ◽

2019 ◽

Cited By ~ 1

Author(s):

Naman Tiwari ◽

Koushik Mondal

Keyword(s):

Machine Learning ◽

Low Power ◽

Image Classification ◽

Machine Learning Techniques ◽

Ultra Low Power ◽

Learning Techniques

Design-space exploration of ultra-low power CMOS logic gates in a 28 nm FD-SOI technology

2017 European Conference on Circuit Theory and Design (ECCTD) ◽

10.1109/ecctd.2017.8093232 ◽

2017 ◽

Author(s):

Marten Vohrmann ◽

Philippe Geisler ◽

Thorsten Jungeblut ◽

Ulrich Ruckert

Keyword(s):

Low Power ◽

Design Space ◽

Space Exploration ◽

Logic Gates ◽

Ultra Low Power ◽

Low Power Cmos ◽

Soi Technology ◽

28 Nm

Experimental exploration of ultra-low power CMOS design space using SOIAS dynamic V/sub T/ control technology

1997 IEEE International SOI Conference Proceedings ◽

10.1109/soi.1997.634940 ◽

2002 ◽

Author(s):

I. Yang ◽

A. Lochtefeld ◽

S. Narendra ◽

A. Chandrakasan ◽

D.A. Antoniadis

Keyword(s):

Low Power ◽

Design Space ◽

Control Technology ◽

Ultra Low Power ◽

Cmos Design ◽

Low Power Cmos