Kneron's Next-Gen AI SoC Processes Video and Audio at the Edge
By Sally Ward-Foxton, EETimes (August 28, 2020)
Kneron, the San Diego- and Taiwan-based AI silicon and IP startup, has launched an AI SoC which features an updated version of the company’s neural processing unit (NPU) IP. The KL720 also features a Cadence DSP AI co-processor and an Arm Cortex M4 core for system control. While Kneron’s next-gen AI SoC is aimed at low-power edge and smart home devices such as video doorbells and robot vacuum cleaners, the KL720 “can be used in everything from a Tesla to a toaster,” according the company.
Kneron claims this second-generation chip outperforms chips from both Intel’s Movidius line and Google’s Coral Edge TPU in terms of energy efficiency. The KL720’s NPU block can perform 1.4 TOPS while the whole SoC, including the additional DSP and Cortex M4 cores, comes in at 0.9 TOPS/W. This is sufficient for processing 4K resolution images and videos up to Full HD 1080p resolution. This compares favorably to Kneron’s previous generation chip, KL520 which was released in May 2019, which could achieve 0.3 TOPS at 0.6 TOPS/W.
To read the full article, click here
Related Semiconductor IP
- NPU
- NPU IP Core for Mobile
- NPU IP Core for Edge
- Specialized Video Processing NPU IP
- NPU IP Core for Data Center
Related News
- Digital Media Professionals (DMP) Unveils Next-Generation Edge AI SoC “Di1” Integrating Advanced AI Inference and Precision Real-Time 3D Ranging Engine
- Redefining Edge AI Development: KNEO Pi Unlocks the Future of Smart Hardware
- Kneron Announces Low Power AI Processors NPU IP Series with the Lowest Power Consumption: Less Than 5mW
- Kneron raises $40m for next-gen Edge AI chip
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