Dr. Kshitij Sudan - Principle Solutions Architect - Arm

Title:

Abstract: Machine learning processing gets a lot of attention due to novel hardware accelerators being developed to speed-up emerging use-cases. The large and rapidly evolving accelerator space for ML processing however is eclipsed in reality by the amount of ML processing that happens on general purpose CPUs. Some estimates rate >80% of ML inference to occur on general-purpose CPUs. The driving factors for on-CPU processing are three fold: 1) Ease of programming, 2) Integration of ML analysis output with business applications, 3) Duty-cycle of ML workloads. In this talk we will first outline the use-cases that are well served by on-CPU ML workload execution followed by how Arm is working to enable more efficient use of general-purpose Arm CPUs for edge-to-cloud processing of ML workloads. Efficient processing requires both hardware and software features to be co-developed – especially since ML algorithms are rapidly evolving. Arm is leveraging this co-design philosophy along with its traditional strength in energy efficient design to make on-CPU ML processing pervasive and easy-to-use.

Keywords: inference, CPU

Bio: Dr. Kshitij Sudan is a Principal Solution Architect in the Infrastructure Business Unit at Arm where he helps build solutions to address market and customer needs. A solution could either be a single piece of Arm IP or a whole platform offering consisting of Arm IP and enabling open-source software stack. His current areas of focus include smart-offload (like SmartNICs), platform security, video encoding, and efficient ML/AI processing. He received his Ph.D. from the University of Utah where his research focused on DRAM-based memory systems. He has been granted two US patents and has multiple applications in the pipeline.