Agency contact Bob Decker Redpines +1 415 409-0233 |
EEMBC contact Peter Torelli, President & CTO EEMBC +1 (203) 423-3179 |
Editor resources:
Link to more
information:
https://www.eembc.org/machine-learning//index.php
EEMBC Seeks
Participants for Machine Learning Working Group
Group Members to Develop Benchmarks for Measuring Performance and
Power Consumption of Processor Cores Running Learning Inference Models
EL DORADO HILLS,
Calif. — May 23, 2018 — EEMBC, an industry consortium that
develops benchmarks for embedded software and hardware, today announced that the organization is seeking
participants for a new Machine Learning
working group. Group members will collaborate to develop EEMBC’s
Machine Learning Benchmark Suite, which will identify the performance potential
and power efficiency of processor cores used for accelerating machine learning jobs on clients such as
virtual assistants, smartphones, and IoT devices.
“Until
now, benchmarks have focused on training processes in the cloud, neglecting
performance and power consumption measurements for cores running learning
inference models on IoT edge devices, such as those used by Amazon Alexa,
Apple’s Siri, and Google Cortana,” said
Peter Torelli, EEMBC president and CTO. “Participants in our Machine Learning
working group will not only help usher in this new and much-needed area of measurement,
but also ensure meaningful and fair representation for their companies’
products.”
Chaired by Intel’s Ramesh Jaladi, the Machine Learning working group is currently defining
the first proofs of concept. Participants include Analog Devices, ARM, AuZone, Flex,
Green Hills Software, Intel, Nvidia, NXP Semiconductors,
Samsung, STMicroelectronics, Synopsys, and Texas Instruments.
For more information on the working group, please
email EEMBC.
# # #
About EEMBC
EEMBC
develops performance benchmarks for the hardware and software used in
autonomous driving, mobile imaging, Internet of Things, scale-out servers,
mobile devices, and many other applications. Benchmark suites are developed in
a consensual process by EEMBC member companies and EEMBC technical staff to
ensure fairness of approach and industry-wide acceptance. Further information
is available at www.eembc.org.
EEMBC
is a registered trademark of the Embedded Microprocessor Benchmark Consortium.
All other trademarks appearing herein are the property of their respective
owners.