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ARC-V RHX-105 dual-issue, 32-bit RISC-V processor for real-time applications (multi-core)
Tensilica Neural Network Compiler: An Offline Tool for Efficient Deployment of Neural Networks
Megha Daga describes how the Tensilica Neural Network Compiler works, from a trained floating-point network to an optimized source code generation for a Tensilica AI-enabled DSP or processor.
Posted on Wednesday Oct. 10, 2018
Cadence Channel
Cadence PCIe 4.0 Receiver JTOL Test
1:43
Verification with Emerging Memory Models
4:12
An Introduction to Palladium Cloud
2:04
xSPI Standard Explained
3:42
The Storage Combo PHY IP - Nirvana!
2:41
What is Happening at the USB IF Standards Meetings?
4:03
Cloud-Hosted Design Solution - a Full-Service Cloud Offering
2:37
The Reason Why the Vision Q7 DSP Should be in Your Vision and AI SoC
5:47
Passport Partners Program Expands Customer Cloud Deployment Options
4:36
Cadence Cloud - Fast, Painless, Proven Solutions for Cloud-Based Design
7:08
Cadence PCIe 4.0 Receiver JTOL Test1:43
Verification with Emerging Memory Models4:12
An Introduction to Palladium Cloud2:04
xSPI Standard Explained3:42
The Storage Combo PHY IP - Nirvana!2:41
What is Happening at the USB IF Standards Meetings?4:03
Cloud-Hosted Design Solution - a Full-Service Cloud Offering2:37
The Reason Why the Vision Q7 DSP Should be in Your Vision and AI SoC5:47
Passport Partners Program Expands Customer Cloud Deployment Options4:36
Cadence Cloud - Fast, Painless, Proven Solutions for Cloud-Based Design7:08