Computing with oscillators from theoretical underpinnings to applications and demonstrators
Aida Todri‐Sanial, Corentin Delacour, Madeleine Abernot et al. · 4 Dec 2024
Networks of coupled oscillators have far-reaching implications across various fields, providing insights into a plethora of dynamics. This review offers an in-depth overview of computing with oscillators covering computational capability, synchronization occurrence and mathematical formalism. We discuss numerous circuit design implementations, technology choices and applications from pattern retrieval, combinatorial optimization problems to machine learning algorithms.…
Edge intelligence through in-sensor and near-sensor computing for the artificial intelligence of things
Yongmin Baek, Byungjoon Bae, Hyo‐Jin Shin et al. · 1 Oct 2025
Artificial intelligence technology transforms traditional sensors from passive data collectors into active computing nodes, performing data processing at the edge. This paradigm shift toward in- and near-sensor computing mitigates inherent inefficiencies associated with data traversal between sensing, memory, and processing units. We introduce emerging device technologies, circuit architectures, algorithmic frameworks, and applications implementing artificial intelligence…
Thermodynamic linear algebra
Maxwell Aifer, Kaelan Donatella, Max Hunter Gordon et al. · 5 Nov 2024
Abstract Linear algebra is central to many algorithms in engineering, science, and machine learning; hence, accelerating it would have tremendous economic impact. Quantum computing has been proposed for this purpose, although the resource requirements are far beyond current technological capabilities. We consider an alternative physics-based computing paradigm based on classical thermodynamics, to provide a near-term…
Advanced AI computing enabled by 2D material-based neuromorphic devices
YunSeok Choi, Siwoo Jeong, Hyeonu Jeong et al. · 21 Apr 2025
Neuromorphic systems are emerging as a promising alternative to revolutionize silicon-based computing devices. Two-dimensional materials are considered promising candidates for active materials due to their unique advantages. This review provides a comprehensive overview of neuromorphic computing based on 2D materials, encompassing the development from the synaptic devices based on 2D materials to the neuromorphic system…
PIM GPT a hybrid process in memory accelerator for autoregressive transformers
Yuting Wu, Ziyu Wang, Wei Lü · 25 Jul 2024
Abstract Decoder-only Transformer models such as Generative Pre-trained Transformers (GPT) have demonstrated exceptional performance in text generation by autoregressively predicting the next token. However, the efficiency of running GPT on current hardware systems is bounded by low compute-to-memory-ratio and high memory access. In this work, we propose a Process-in-memory (PIM) GPT accelerator, PIM-GPT, which achieves…
