Specialized hardware accelerators are abundantly available today, including NPUs found in consumer laptops with AMD Ryzen™ AI CPUs. The NPU on AMD Ryzen™ AI includes an AI Engine array comprised of a set of VLIW vector processors, data movement accelerators (DMAs), and adaptable interconnect. By providing convenient software tool flows to program these devices, enthusiasts are enabled to productively harness the full capabilities of these powerful NPUs. IRON (Interface Representations for hands-ON programming of Fast and Efficient AIE designs) is a close-to-metal open-source toolkit enabling performance engineers to build fast and efficient, often specialized, designs through a set of Python language bindings around the mlir-aie dialect. The presentation will first provide insights into the AI Engine compute and data movement capabilities supported in our tool flow. Then, the speakers will demonstrate performance optimizations of increasingly complex designs, including machine learning models, weather forecasting simulations, and genome sequencing. We exploit the two-dimensional spatial architecture to efficiently accelerate end-to-end workloads using IRON. Our implementation reveals that balancing workload across the available processing resources is crucial in achieving high performance on spatial architectures. These use cases highlight how the architectural features of the AI Engines, combined with IRON's capabilities, enable significant performance improvements and efficient resource utilization.
Bio's:
Gagandeep Singh is a Researcher at AMD’s Research and Advanced Development Group, focusing on hardware acceleration and performance modeling. Prior to joining AMD, he was a Postdoctoral Researcher at ETH Zürich in SAFARI Research Group. He received his Ph.D. from TU Eindhoven in collaboration with IBM Research Zürich in 2021. In 2017, Gagan received a joint M.Sc. degree with distinction in Integrated Circuit Design from TUM, Germany, and NTU, Singapore. He did his master's thesis at IMEC, Belgium, on architecture modeling and design space exploration for 3D stacked interconnect technology, for which he received the best thesis award. Gagan was also an R&D Software Developer at Oracle, India. He has published several research papers in prestigious conferences and journals, including ISCA, MICRO, IEEE Micro, Genome Biology, and Bioinformatics. He is passionate about computer architecture, hardware acceleration, and machine learning.
Kristof Denolf is a Fellow in AMD's Research and Advanced Development group where he is working on energy-efficient computer vision and video processing applications to shape future AMD devices. He earned an M.Eng. in electronics from the Katholieke Hogeschool Brugge-Oostende (1998), now part of KULeuven, an M.Sc. in electronic system design from Leeds Beckett University (2000), and a Ph.D. from the Technical University Eindhoven (2007). He has over 25 years of combined research and industry experience at IMEC, Philips, Barco, Apple, Xilinx, and AMD. His main research interests are all aspects of the cost-efficient and dataflow-oriented design of video, vision, and graphics systems.
18/10/2024 11:00 - 12:00
ESAT Aula L
