AI Startup Wave Computing To Buy MIPS
Wave to expand from AI training to AI inference
Junko Yoshida, EETimes
6/13/2018 03:01 PM EDT
MADISON, Wis. — EE Times has learned that Wave Computing (Campbell, California), a startup focused on the development of AI systems using its massively parallel dataflow architecture, will announce later this week that it has acquired MIPS Technologies, a storied but beleaguered Silicon Valley company that developed MIPS processor architecture and related processing cores.
The move will allow Wave Computing to expand from AI training in data centers to AI inference for embedded systems.
EE Times called Wave Computing's CEO Derek Meyer, and asked him what has motivated the startup to make such a move.
He told EE Times, “Our strategy has always been to push our dataflow fabric out to the edge.” The acquisition should give Wave Computing an opportunity to infuse its scalable dataflow technology with MIPS’s RISC processors while plugging it into MIPS’s ecosystem.
To read the full article, click here
Related Semiconductor IP
- 100-10Gbps Ethernet MAC Controller
- SD Card Controller
- AXI4 Memory Controller IP Core
- AXI-Lite I2C Controller Core
- LVDS Transceiver IP core
Related News
- Wave Computing Adopts Low Power MIPS 64-bit Multi-Threaded Core
- Wave Computing Extends AI Lead by Targeting Edge of Cloud Through Acquisition of MIPS
- Wave Computing Turbo Boosts "MIPS" with Licensable AI Subsystems, An Expanded Ecosystem & New Product Roadmap
- Wave Computing Appoints Industry Veteran Art Swift As President of its Recently Acquired MIPS Licensing Business
Latest News
- BrainChip Launches AKD1500 Raspberry Pi 5 Kit: Giving Developers a Direct Path from Prototype to Production
- Cadence Joins the Genesis Mission Consortium to Advance AI-Driven Scientific Discovery and Microelectronics Innovation
- Onsemi picks up Codasip’s low-end RISC-V business for $14.5 million
- Semidynamics and Nokia Explore Collaboration to Strengthen Europe’s Sovereign AI Capabilities
- VeriSilicon Introduces GLTiny™ 3D GPU Programming Model for Ultra-Low-Power, Memory-Efficient Wearable Devices