Results 201 to 210 of about 7,322 (252)

Toward Capacitive In‐Memory‐Computing: A Device to Systems Level Perspective on the Future of Artificial Intelligence Hardware

Advanced Intelligent Discovery, EarlyView.
Capacitive, charge‐domain compute‐in‐memory (CIM) stores weights as capacitance,eliminating DC sneak paths and IR‐drop, yielding near‐zero standbypower. In this perspective, we present a device to systems level performance analysis of most promising architectures and predict apathway for upscaling capacitive CIM for sustainable edge computing ...
Kapil Bhardwaj, Ella Paasio, Sayani Majumdar   +2 more
wiley   +1 more source

The neurobench framework for benchmarking neuromorphic computing algorithms and systems. [PDF]

Nat Commun
Yik J, Van den Berghe K, den Blanken D, Bouhadjar Y, Fabre M, Hueber P, Ke W, Khoei MA, Kleyko D, Pacik-Nelson N, Pierro A, Stratmann P, Sun PV, Tang G, Wang S, Zhou B, Ahmed SH, Vathakkattil Joseph G, Leto B, Micheli A, Mishra AK, Lenz G, Sun T, Ahmed Z, Akl M, Anderson B, Andreou AG, Bartolozzi C, Basu A, Bogdan P, Bohte S, Buckley S, Cauwenberghs G, Chicca E, Corradi F, de Croon G, Danielescu A, Daram A, Davies M, Demirag Y, Eshraghian J, Fischer T, Forest J, Fra V, Furber S, Furlong PM, Gilpin W, Gilra A, Gonzalez HA, Indiveri G, Joshi S, Karia V, Khacef L, Knight JC, Kriener L, Kubendran R, Kudithipudi D, Liu SC, Liu YH, Ma H, Manohar R, Margarit-Taulé JM, Mayr C, Michmizos K, Muir DR, Neftci E, Nowotny T, Ottati F, Ozcelikkale A, Panda P, Park J, Payvand M, Pehle C, Petrovici MA, Posch C, Renner A, Sandamirskaya Y, Schaefer CJS, van Schaik A, Schemmel J, Schmidgall S, Schuman C, Seo JS, Sheik S, Shrestha SB, Sifalakis M, Sironi A, Stewart K, Stewart M, Stewart TC, Timcheck J, Tömen N, Urgese G, Verhelst M, Vineyard CM, Vogginger B, Yousefzadeh A, Zohora FT, Frenkel C, Reddi VJ.   +99 more
europepmc   +1 more source

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

An Energy Efficient Memory Cell for Quantum and Neuromorphic Computing at Low Temperatures. [PDF]

Nano Lett
Han Y, Sun J, Richstein B, Grenmyr A, Bae JH, Allibert F, Radu I, Grützmacher D, Knoch J, Zhao QT.   +9 more
europepmc   +1 more source

Haptic In‐Sensor Computing Device Based on CNT/PDMS Nanocomposite Physical Reservoir

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Using a porous carbon nanotube‐polydimethylsiloxane nanocomposite, a sensor array integrated with a physical reservoir computing paradigm capable of in‐sensor computing is demonstrated. The device is able to classify between nine objects with an accuracy above 80%, opening the possibility for low‐power sensing/computing for future robotics.
Kouki Kimizuka, Saman Azhari, Shoshi Tokuno, Ahmet Karacali, Yuki Usami, Shuhei Ikemoto, Hakaru Tamukoh, Hirofumi Tanaka   +7 more
wiley   +1 more source

Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity. [PDF]

Nat Commun
Maryada, Soldado-Magraner S, Sorbaro M, Laje R, Buonomano DV, Indiveri G.   +5 more
europepmc   +1 more source

Investigation of Analog Memristor Characteristics for Hardware Synaptic Weight in Multilayer Neural Network

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The systematic design of memristor‐based neural network is provided by analog conductance state parameters to accurately emulate the software‐based high‐resolution weight at discrete device level. The requirement of discrete analog conductance of memristor device is measured as ≈50 states with nonlinearity value of ≈0.142 within the deviation range of ...
Jingon Jang, Yoonseok Song, Sungjun Park
wiley   +1 more source

Dual SOT Switching Modes in a Single Device Geometry for Neuromorphic Computing. [PDF]

Nano Lett
Ranjan A, Farooq T, Chi CC, Sung HY, Salinas Padilla RI, Lin PH, Wu WW, Lu MY, Mishra R, Lai CH.   +9 more
europepmc   +1 more source

Self-assembled 3D Interconnected Magnetic Nanowire Networks for Neuromorphic Computing. [PDF]

ACS Appl Mater Interfaces
Bhattacharya D, Langton C, Rajib MM, Marlowe E, Chen Z, Al Misba W, Atulasimha J, Zhang X, Yin G, Liu K.   +9 more
europepmc   +1 more source

Coupling Light into Memristors: Advances in Halide Perovskite Resistive Switching and Neuromorphic Computing. [PDF]

Small Methods
Feng Z, Wang J, Chen F, Dong B, Ma X, Mei T, Yang N, Guan X, Hu L, Lin CH, Li Z, Wu T, Chu D.   +12 more
europepmc   +1 more source