Hardware for Deep Learning. Part 4: ASIC | by Grigory Sapunov | Intento
Convolutional Neural Network (CNN) processor design on VHDL/Verilog - YouTube
GitHub - coleblackman/TIDENet: TIDENet is an ASIC written in Verilog for Tiny Image Detection at Edge with neural networks (TIDENet) using DNNWeaver 2.0, the Google SkyWater PDK, OpenLANE, and Caravel.
Power and throughput among CPU, GPU, FPGA, and ASIC. | Download Scientific Diagram
PDF] ASIC Implementation of Neural Network Based Image Compression | Semantic Scholar
Machine Learning: How HLS Can Be Used to Quickly Create FPGA/ASIC HW for a Neural Network Inference Solution
An on-chip photonic deep neural network for image classification | Nature
Comparison of neural network accelerators for FPGA, ASIC and GPU... | Download Scientific Diagram
PDF] ASIC Implementation of Neural Network Based Image Compression | Semantic Scholar
PDF] Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC | Semantic Scholar
Hardware for Deep Learning. Part 4: ASIC | by Grigory Sapunov | Intento
Hardware Acceleration of Deep Neural Network Models on FPGA ( Part 1 of 2) | ignitarium.com
Are ASIC Chips The Future of AI?
GitHub - zssloth/Embedded-Neural-Network: collection of works aiming at reducing model sizes or the ASIC/FPGA accelerator for machine learning
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC - CERN Document Server
Designing With ASICs for Machine Learning in Embedded Systems | NWES Blog
How to develop high-performance deep neural network object detection/recognition applications for FPGA-based edge devices - Blog - Company - Aldec
Hardware for Deep Learning. Part 4: ASIC | by Grigory Sapunov | Intento
14/16nm ASIC Design | UW Department of Electrical & Computer Engineering
Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC
A reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC: Paper and Code - CatalyzeX
Arch-Net: A Family Of Neural Networks Built With Operators To Bridge The Gap Between Computer Architecture of ASIC Chips And Neural Network Model Architectures - MarkTechPost
FPGA Based Deep Learning Accelerators Take on ASICs