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Machine Learning and AI are experiencing explosive growth. The concentration of AI power in the datacenters, as well as the current trends in training and inferencing infrastructure built around power-hungry GPUs and control nodes running interpretive languages on full-fledged monolithic operating systems brings about an evermore greater need for energy. In this talk we will sketch a different future: one where the needs of scaling are addressed in a way of embedding lightweight control software running on energy-efficient hardware into a sea of heterogeneous compute accelerators arranged in an energy-conserving fashion. One where the edge devices, be it small IoT nodes or an intelligence subsystem inside a mobile device can all be included in one global, distributed, cognitive and sustainable network supporting the users. We intend to cover our recent developments in the way of porting IREE to run on Zephyr's POSIX layer, as well as experiments to see how Zephyr as a lightweight library kernel can support typical inference tasks that were used as workloads to tune an accelerator's micro-architecture (using gem5 simulator) as well as to emulate the design on the FPGA as a scale-up.
Having started my professional career into CS (at TUDelft DBMS) I moved towards RT & streaming systems for CE (architecture & infra group at Philips Research). Dataflow compiler & middleware project got me further into the DSPs and models for radio & radar transceivers - the focus... Read More →
