FPGAs take on convolutional neural networks
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.
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
- Why Choose Hard IP for Embedded FPGA in Aerospace and Defense Applications
- Migrating the CPU IP Development from MIPS to RISC-V Instruction Set Architecture
- Quintauris: Accelerating RISC-V Innovation for next-gen Hardware
- Say Goodbye to Limits and Hello to Freedom of Scalability in the MIPS P8700
- Why is Hard IP a Better Solution for Embedded FPGA (eFPGA) Technology?