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MIPI M-PHY G4 Type 1 2Tx2RX in TSMC (16nm, 12nm, N7, N6, N5, N4, N3A, N3E)
Complexity Optimization of Convolutional Neural Networks: Overview
Raul Casas, systems architect IP group, talks about machine learning moving from servers to the edge where the power consumption budget is critical. Cadence developed a method for reduction of complexity of a convolutional neural network.
Posted on Wednesday Sep. 27, 2017
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