Chairs:  Andreas Burg (EPFL) and Marian Verhelst (KU Leuven)

Full content duration ~6h

 

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a) Introduction Into In-Memory Computing and Efficient AI Processing at the Edge

Jae-sun Seo (Arizona State University, USA)

 

b) Mixed-signal processing opportunities for AI

Boris Murmann (Stanford University, USA)

 

c) Computational-SRAM: A Low-Latency & Energy-Efficient Vector Processing for Edge AI applications

Maha Kooli (CEA-LETI, France)

 

d) Co-Designing Memory Technology and ML Models for the Edge

Paul Whatmough (ARM Research, UK)

 

e) Compute-in-Memory for Deep Learning Accelerator: Challenges and Prospects

Shimeng Yu (Georgia Tech. USA)

 

f) Novel memory devices tailored to Analog in-Memory Computing for Neural Network inference

Stefan Cosemans (imec, Belgium)

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g) In-memory Computing: from fundamentals to complete architectures

Naveen Verma (Princeton University, USA)

 

h) 3D Integration and Nano-Technologies for Higher Performance Artificial Intelligence Systems

Volkan Özgüz (Sabanci University, Turkey)

 

i) AI hardware acceleration - from cloud to edge

Kyu-hyoun Kim (IBM, USA)

 

j) An Industrial perspective on AI@Edge with elements of In-Memory Compute as system and technology co-optimization opportunities

Giuseppe Desoli & Nitin Chawla (STMicroelectronics, Italy)

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