FPGAs take on convolutional neural networks
May 8, 2017
In the context of machine learning, a convolutional neural network (CNN, or ConvNet) can perhaps best be defined as a category of feed-forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex. According to Stanford staff, convolutional Neural Networks are quite similar to ordinary neural networks, as they are comprised of neurons that have learnable weights and biases.
To read the full article, click here
Related Semiconductor IP
Related Blogs
- Embedded Vision: The Road Ahead for Neural Networks and Five Likely Surprises
- Push-button generation of deep neural networks
- Hierarchical Neural Networks
- Deployable Artificial Neural Networks Will Change Everything
Latest Blogs
- Embedded Security explained: Advanced Encryption Standard (AES)
- Cadence Demonstrates PCIe 8.0 PHY at PCI-SIG DevCon 2026
- Cadence Achieves Successful Silicon Validation of 1st IP Test Chips on Intel 18A
- From Classical CAN and CAN FD to CAN XL: Functional Safety and Security for Next-Generation In-Vehicle Communication
- Accelerating Embedded Memory Performance with 16-bit xSPI PSRAM IP