Technology Spotlight

 

The Era of AI Hardware

At the recent IEEE Industry Summit on the Future of Computing, held in Washington DC as part of IEEE Rebooting Computing Week, one of the keynote talks was by Dr. Mukesh Khare, Vice President of Semiconductor Research, IBM Research. A brief introductory video of IBM’s efforts in this field is given here. Dr. Khare’s talk is available here.

The concurrent evolution of broad AI with purpose-built hardware will shift traditional balances between cloud and edge, structured and unstructured data, and training and inference. Distributed deep learning approaches, coupled with heterogeneous system architectures effectively address bandwidth, latency, and scalability requirements of complex AI models. Hardware, purpose-built for AI, holds the potential to unlock exponential gains in AI computations. IBM Research is making further strides in AI hardware through the use of Digital AI Cores using approximate computing, non-Von-Neumann approaches with Analog AI Cores, and the emergence of quantum computing for AI workloads.

Videos of other Industry Summit 2018 invited talks are available from IEEE.tv.

 

Big Data Meets Big Compute

At the recent IEEE Industry Summit on the Future of Computing, held in Washington DC as part of IEEE Rebooting Computing Week, one of the keynote talks was by Alan Lee, Corporate Vice President, Head of Research, and Head of Deployed AI and Machine Learning Technologies at Advanced Micro Devices (AMD). The video of Mr. Lee’s talk is available here.

Mr. Lee spoke about “Big Data Meets Big Compute.” The volume of data being generated is rising exponentially, much faster than the growth in computing speed. Furthermore, the types of data are quite heterogeneous, as are the types of analysis, which will include artificial intelligence and machine learning (AI/ML). In order for data centers and supercomputers to handle this efficiently, they will need to incorporate a broad range of processors on the hardware level, as well as a complete range of algorithms and applications software. While custom solutions are most efficient in principle, the custom development effort is generally impractical. Mr. Lee recommended a modular approach at multiple levels in the stack. This could include chip-level modularity, whereby chiplets incorporating different processors (CPUs, GPUs, and FPGAs) and memory could be integrated in a semi-custom way on the same multi-chip module. Similarly, one could incorporate open-source software modules that could interface efficiently with the range of hardware. In this way, one can expect to obtain many of the benefits of custom design while minimizing some of the difficulties in programming and testing. The transition to this heterogeneous computing environment has already begun, and will likely continue for at least the next decade.

Videos of other Industry Summit 2018 invited talks are available from IEEE.tv.