Segmenting the Machine-Learning Hardware Market
One of the great pleasures in what I do is to work with people who are working with people in some of the hottest design areas today. A second-level indirect to be sure but that gives me the luxury of taking a broad view. A recent discussion I had with Kurt Shuler (VP Marketing at Arteris IP) is in this class. As a conscientious marketing guy, he wants to understand the available market in AI hardware because they have quite a bit of activity in that space – more on that later.
So Kurt put a lot of work into finding every company and product he could that is active in this space, 91 entries in his spreadsheet. This he broke down by company, territory (eg China or US), product, target market (eg vision or speech), implementation (eg FPGA or ASIC), whether the product is used in datacenters or at the edge and whether it is being used for training or inference. I’ll share some interesting observations from the list but not the list itself. Kurt told me he put a lot of work into building this list, so I can’t imagine he be excited about giving it away
To read the full article, click here
Related Blogs
- Trust at the Core: A Deep Dive into Hardware Root of Trust (HRoT)
- Evaluating the Side Channel Security of Post-Quantum Hardware IP
- The Silent Guardian of AI Compute - PUFrt Unifies Hardware Security and Memory Repair to Build the Trust Foundation for AI Factories
- Universal Browser Support for JPEG XL: Is Your Hardware Ready for the New Standard?
Latest Blogs
- A Repeatable Framework for Hardware Security Assurance
- Inside the SiFive Performance™ P570 Gen 3: High Performance Efficiency for Next-Generation Consumer and Commercial Applications
- What the steam engine can teach us about modern chip design
- Automotive silicon in the era of AI, functional safety, and cybersecurity
- JPEG XS Officially Joins GenICam, The Machine Vision Standard Managed By EMVA