Machine learning interview with Jem Davies of Arm
February 15, 2018 // By Peter Clarke, eeNews
Processor IP licensor ARM has announced a dedicated machine learning processor core and eeNews Europe spoke with Jem Davies, general manager of the machine learning group at ARM, and Dennis Laudick, vice president of marketing for machine learning (ML), to find out more.
Davies recently migrated from leading ARM's graphics and vision business.
Davies started our interview by making the point that machine learning computation by way of neural networks is a fundamental shift in computation and that ARM has been taking its time to try and make sure its architectural approach is sufficiently general and scalable to have a long life in the market. It has now completed designing the first hardware implementation, the ML processor, which it will distribute to licensees sometime in the middle of 2018. It is also offering an iteration of its object detection image processor; both under the Project Trillium banner.
To read the full article, click here
Related Semiconductor IP
- NPU IP Core for Mobile
- NPU IP Core for Edge
- Specialized Video Processing NPU IP
- HYPERBUS™ Memory Controller
- AV1 Video Encoder IP
Related News
- Thoughts on Jem Davies leading Arm's machine learning group
- Baidu Adopts Xilinx to Accelerate Machine Learning Applications in the Data Center
- Netspeed Raises $10M in Series C Funding Led by Intel Capital to Bring Machine learning to SoC design and architecture
- Xilinx FPGAs to be Deployed in New Amazon EC2 F1 Instances - Accelerating Genomics, Financial Analytics, Video Processing, Big Data, Security, and Machine Learning Inference
Latest News
- Jim Keller: ‘Whatever Nvidia Does, We’ll Do The Opposite’
- FlexGen Streamlines NoC Design as AI Demands Grow
- IntoPIX Presents Its New Titanium Software Suite: Empowering AV-Over-IP Workflows With Speed, Quality & Interoperability
- Global Semiconductor Sales Increase 2.5% Month-to-Month in April
- Speedata Raises $44M to Launch First-Ever Chip Designed Specifically for Accelerating Big Data Analytics - Compute's Second Largest Workload