Research topics on Artificial Intelligence (AI) accelerator designs for edge devices have attracted vast interest, where accelerating Deep Neural Networks (DNN) using Processing-in-Memory (PIM) and Processing-in-Sensor (PIS) platforms is an activelyexplored direction with great potential. Such accelerators, which simultaneously aim to address power- and memory-wall bottlenecks, have shown orders of performance enhancement in comparison to the conventional computing platforms with Von-Neumann architecture. As one direction of accelerating DNN in PIM/PIS, resistive memory array (aka. crossbar) has drawn great research interest owing to its analog current mode weighted summation operation which intrinsically matches the dominant Multiplicationand-Accumulation (MAC) operation in DNN, making it one of the most promising candidates. An alternative direction is through bulk bit-wise logic operations directly performed on the content in digital memories.
The main goal of this seasonal school is to dive deep into the rapidly developing field of PIM and PIS with a focus on the intelligent memory and sensor circuits and systems at the edge and cover its cross-layer design challenges from device to algorithms. The IEEE Seasonal School in Circuits and Systems on Intelligent Memory & Sensor at the Edge (IMS 2023) offers talks and tutorials by leading researchers from multiple disciplines and prominent universities and promotes student short presentations to demonstrate new research and results, discuss the potential and challenges of the edge accelerators, future research needs, and directions, and shape collaborations.