Dr. Teuscher presented his paper on “Material and Physical Reservoir Computing for Beyond CMOS Electronics: Quo Vadis?” at the 18th ACM International Symposium on Nanoscale Architectures (NANOARCH 23), which was held in Dresden, Germany, Dec 18-20, 2023.
Christof Teuscher gave a tutorial on “Material and Physical Reservoir Computing for Beyond-CMOS Electronics” at the IEEE International Symposium on Circuits and Systems (ISCAS).
ISCAS is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world’s premiere forum for researchers in the active fields of theory, design and implementation of circuits and systems.
Tutorial slides, introductory reading, and a bibliography are available at ISCAS 2023 Tutorial
More info about ISCAS tutorials at https://iscas2023.org/tutorials
Christof Teuscher will be teaching a tutorial on “Material and Physical Reservoir Computing for Beyond-CMOS Electronics” at the IEEE International Symposium on Circuits and Systems (ISCAS).
ISCAS is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world’s premiere forum for researchers in the active fields of theory, design and implementation of circuits and systems.
Tutorial abstract:
Traditional computing is based on an engineering approach that imposes logical states and a computational model upon a physical substrate. Physical or material computing, on the other hand, harnesses and exploits the inherent, naturally-occurring properties of a physical substrate to perform a computation. To do so, reservoir computing is often used as a computing paradigm. In this tutorial, you will learn what reservoir computing is and how to use it for computing with emerging devices and fabrics. You will also learn about the current state-of-the-art and what opportunities and challenges for future research exist. The tutorial is relevant for anybody interested in beyond-CMOS and beyond-von-Neumann architectures, ML, AI, neuromorphic systems, and computing with novel devices and circuits.
More info about ISCAS tutorials at https://iscas2023.org/tutorials
D. Tran and C. Teuscher, Multi-tasking Memcapacitive Networks, in IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2023. doi: 10.1109/JETCAS.2023.3235242.
Abstract:
Recent studies have shown that networks of memcapacitive devices provide an ideal computing platform of low power consumption for reservoir computing systems. Random, crossbar, or small-world power-law (SWPL) structures are common topologies for reservoir substrates to compute single tasks. However, neurological studies have shown that the interconnections of cortical brain regions associated with different functions form a rich-club structure. This structure allows human brains to perform multiple activities simultaneously. So far, memcapacitive reservoirs can perform only single tasks. Here, we propose, for the first time, cluster networks functioning as memcapacitive reservoirs to perform multiple tasks simultaneously. Our results illustrate that cluster networks surpassed crossbar and SWPL networks by factors of 4.1×, 5.2×, and 1.7× on three tasks: Isolated Spoken Digits, MNIST, and CIFAR-10. Compared to single-task networks in our previous and published results, multitasking cluster networks could accomplish similar accuracies of 86%, 94.4%, and 27.9% for MNIST, Isolated Spoken Digits, and CIFAR-10. Our extended simulations reveal that both the input signal amplitudes and the inter-cluster connections contribute to the accuracy of cluster networks. Selecting optimal values for signal amplitudes and inter-cluster links is key to obtaining high classification accuracy and low power consumption. Our results illustrate the promise of memcapacitive brain-inspired cluster networks and their capability to solve multiple tasks simultaneously. Such novel computing architectures have the potential to make edge applications more efficient and allow systems that cannot be reconfigured to solve multiple tasks.