Search Results for author:
Found 3 papers, 1 papers with code
Efficient Error-Tolerant Quantized Neural Network Accelerators
In particular, Convolutional Neural Networks (CNNs), are gaining popularity and are evaluated for deployment in safety critical applications such as self driving vehicles.
AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers
Low-precision arithmetic operations to accelerate deep-learning applications on field-programmable gate arrays (FPGAs) have been studied extensively, because they offer the potential to save silicon area or increase throughput.
Unrolling Ternary Neural Networks
The computational complexity of neural networks for large scale or real-time applications necessitates hardware acceleration.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
