Amr Adel
Amr is a young digital design and ASIC implementation engineer with experience working at multinational companies on various projects and products. He enjoys teaching and creating training content on topics such as deep learning acceleration on hardware, DSP, and other engineering subjects.
His graduation project focused on designing and implementing an AI accelerator. This project aimed to create specialized hardware that could efficiently accelerate deep-learning computations. This project sparked his deep interest in deep-learning acceleration and hardware optimization, which he continues to explore in his professional career and teaching efforts.
Amr holds a Bachelor's degree in Electrical and Communication Engineering from Cairo University.
Implementing a Convolutional Neural Network (CNN) Layer on Hardware
Status: Not yet available - Stay tuned!In this talk, we will explore the ways of implementing convolutional neural network (CNN) layers on hardware platforms. As deep learning continues to drive advancements in various fields, the need for efficient and high-performance hardware implementations becomes critical. We will delve into the architectural considerations, including data flow, parallel processing, and memory optimization, necessary for translating CNNs from software to hardware.
Starting with an overview of CNN operations, we will discuss fixed-point arithmetic and its advantages for hardware efficiency. We will then demonstrate a practical example of implementing a CNN layer on an FPGA, highlighting the steps from algorithmic design to hardware synthesis and deployment.
The talk will also cover optimization techniques to enhance throughput and reduce latency, such as parallelism and pipelining. Real-world case studies will illustrate the performance gains and energy efficiency improvements achieved through hardware acceleration of CNNs. By the end of the session, participants will have a comprehensive understanding of the challenges and solutions in implementing CNN layers on hardware, equipping them with the knowledge to embark on their own hardware acceleration projects.