Photonic devices, ASICs and systems for machine intelligence

Volker J. Sorger

doi:10.1117/12.2632659

3 October 2022 Photonic devices, ASICs and systems for machine intelligence

Volker J. Sorger

Proceedings Volume PC12196, Active Photonic Platforms 2022; PC121960V (2022) https://doi.org/10.1117/12.2632659
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

The high demand for AI services in conjunction with a dramatic chip shortage along with technology leaps such as 5/6G networks, cybersecurity threats, and quantum algorithms have resurrected a R&D push for advanced information processing and computing capability. To address this demand and explore novel technology roadmaps, unique opportunities exist, for example, given by algorithmic parallelism of digital-analog hybrid non-van Neuman accelerators. Especially photonic-electronic ASIC compute paradigms hold promise to enable non-iterative O(1) runtime complexity, ps-short latency, and TOPS/W throughputs. This opens prospects for next-generation hardware both for AI cloud services but also for accelerating edge computing such as enabled by compact and efficient PIC-CMOS co-designs pushing the SWAP envelope. As both a professor and a co-founder of a deep-tech venture, in this seminar I will share my insights on fundamental complexity scaling and algorithm-hardware homomorphism on the one hand, and system-level synergies and co-design optimization strategies on the other. I will introduce a novel photonic RAM capable of zero-static power consumption suitable for edge applications and a mixed-signal tensor core accelerator leveraging parallelism. Beyond matrix-matrix multiplication acceleration, I will show how convolutions can be accelerated as simple dot-product multiplications in the Fourier domain and using display light technology enables 1000x1000 matrix convolutions at 100us latency, or about 10x faster than today’s GPUs. Finally, I will share latest opto-electronic device work such as 20Gbps ITO-based modulators that are 1,000x more compact than Silicon and LN modulators, and discuss scaling-length-theory slot-detectors featuring zero-bias detector and low NEPs.

Conference Presentation

Citation Download Citation

Volker J. Sorger "Photonic devices, ASICs and systems for machine intelligence", Proc. SPIE PC12196, Active Photonic Platforms 2022, PC121960V (3 October 2022); https://doi.org/10.1117/12.2632659

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KEYWORDS

Intelligence systems

Photonic devices

Artificial intelligence

Convolution

Modulators

Optoelectronics

Quantum computing

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