Machine Learning Benchmark Suite
This EEMBC benchmark suite will use real-world workloads to 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.
Call for Participation
According to recent press coverage1, more than 10 processor cores built to accelerate machine learning tasks on virtual assistants and IoT devices are competing for spots in SoCs, but the industry is still waiting for benchmarks that can show which of these chips delivers the best combination of performance, power, and die area.
EEMBC is currently seeking members for a new working group that will develop Machine Learning benchmarks that will serve as a vendor-neutral industry standard for measuring the performance and power consumption of cores running learning inference models on IoT edge devices. Examples of clients where these cores are used include Amazon Alexa, Apple’s Siri, and Google Cortana. The new EEMBC suite will thus open up a new area of performance measurement that until now has been neglected in favor of benchmarks that focus mainly on training processes in the cloud.
Examples of Potential Benchmark Targets
- Almotive Alware
- Cadence Vision P6
- Cambricon CPU
- Ceva NeuPro
- Imagination PowerVR 2NX
- Nvidia NVDLA
- Synopsys EV64
- VeriSilicon VIP
- Videantis v-MP6000
Working Group Participants as of June 2018
- Analog Devices
- Green Hills Software
- Texas Instruments
Working Group Status
- Currently defining the first proof of concept
- Join the EEMBC Machine Learning working group to help ensure a meaningful and fair representation for your company’s products. Email EEMBC for more information, or learn about becoming a member.
- Ramesh Jaladi, Intel