Hardware for Deep Learning. Part 4: ASIC | by Grigory Sapunov | Intento
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
Algorithms | Free Full-Text | A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing | HTML
Intel Speeds AI Development, Deployment and Performance with New Class of AI Hardware from Cloud to Edge | Business Wire
How to Develop High-Performance Deep Neural Network Object Detection/Recognition Applications for FPGA-based Edge Devices - Embedded Computing Design
A Breakthrough in FPGA-Based Deep Learning Inference - EEWeb
Why ASICs Are Becoming So Widely Popular For AI
How to make your own deep learning accelerator chip! | by Manu Suryavansh | Towards Data Science
Deep Neural Network ASICs The Ultimate Step-By-Step Guide by Gerardus Blokdyk - Ebook | Scribd
Steve Blank Artificial Intelligence and Machine Learning– Explained
Leveraging FPGAs for deep learning - Embedded.com
My take on the Gartner Hype Cycle | by Jens Møllerhøj | Medium
Hardware for Deep Learning. Part 4: ASIC | by Grigory Sapunov | Intento
Comparison of neural network accelerators for FPGA, ASIC and GPU... | Download Scientific Diagram
Future Internet | Free Full-Text | An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks
Embedded Hardware for Processing AI - ADLINK Blog
How to make your own deep learning accelerator chip! | by Manu Suryavansh | Towards Data Science
FPGA-based Accelerators of Deep Learning Networks for Learning and Classification: A Review
Hardware for Deep Learning. Part 4: ASIC | by Grigory Sapunov | Intento
Are ASIC Chips The Future of AI?
Stepping into the future with ASICS technologies | CMC Global Consulting
Deep Learning Has Hit a Wall, Intel's Rao Says
Deploy ML models to FPGAs - Azure Machine Learning | Microsoft Learn
Blog: Aldec Blog - How to develop high-performance deep neural network object detection/recognition applications for FPGA-based edge devices - FirstEDA
8-Bit Precision for Training Deep Learning Systems | IBM Research Blog
