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
- Cadence Announces Industry's First Verification IP for Embedded USB2v2 (eUSB2v2)
- The Industry’s First USB4 Device IP Certification Will Speed Innovation and Edge AI Enablement
- Understanding Extended Metadata in CXL 3.1: What It Means for Your Systems
- 2025 Outlook with Mahesh Tirupattur of Analog Bits
- eUSB2 Version 2 with 4.8Gbps and the Use Cases: A Comprehensive Overview