21 papers · ranked by Valyu relevance
Giacomo Indiveri, B. Linares-Barranco, Robert Legenstein, G. Deligeorgis + 1 more
'G. Deligeorgis' 'Themistoklis Prodromakis'] Abstract. Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largely ignored key features of biological neural processing systems, such as their…
Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J.D. Birdwell + 3 more
'J.D. Birdwell' 'Mark E. Dean' 'Garrett S. Rose' 'James S. Plank'] Abstract—Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic…
Ruslan V. Kutluyarov, Aida G. Zakoyan, Grigory S. Voronkov, Elizaveta P. Grakhova + 2 more
Neuromorphic photonics is a cutting-edge fusion of neuroscience-inspired computing and photonics technology to overcome the constraints of conventional computing architectures. Its significance lies in the potential to transform information processing by mimicking the parallelism and efficiency of the human brain.…
Zhengguang Zhu, Nicholas Schaffer, Xiao Yang
Neuromorphic devices are bioinspired electronic systems that mimic key structures and functions of the nervous system, enabling integration and communication between living tissues and machines. This review examines how neuromorphic devices and computing are designed to emulate the structure, organization, and function…
Chetan Singh Thakur, Jamal Lottier Molin, Gert Cauwenberghs, Giacomo Indiveri + 11 more
'Giacomo Indiveri' 'Kundan Kumar' 'Ning Qiao' 'Johannes Schemmel' 'Runchun Wang' 'Elisabetta Chicca' 'Jennifer Olson Hasler' 'Jae-sun Seo' 'Shimeng Yu' 'Yu Cao' 'André van Schaik' 'Ralph Etienne-Cummings'] Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are…
Dmitry Ivanov, Aleksandr Chezhegov, Mikhail Kiselev, Andrey Grunin + 1 more
'Denis Larionov'] Modern artificial intelligence (AI) systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the mammalian brain. In this article we discuss these limitations and ways to mitigate them. Next, we present an overview of…
Luis A. Camuñas-Mesa, Bernabé Linares-Barranco, Teresa Serrano-Gotarredona
'Teresa Serrano-Gotarredona'] Inspired by biology, neuromorphic systems have been trying to emulate the human brain for decades, taking advantage of its massive parallelism and sparse information coding. Recently, several large-scale hardware projects have demonstrated the outstanding capabilities of this paradigm for…
Adnan Mehonić, Daniele Ielmini, Kaushik Roy, Onur Mutlu + 49 more
'Shahar Kvatinsky' 'Teresa Serrano‐Gotarredona' 'B. Linares-Barranco' 'Sabina Spiga' 'Sergey Savel’ev' 'A. G. Balanov' 'Nitin Chawla' 'Giuseppe Desoli' 'Gerardo Malavena' 'Christian Monzio Compagnoni' 'Zhongrui Wang' 'J. Joshua Yang' 'Syed Ghazi Sarwat' 'Abu Sebastian' 'Thomas Mikolajick' 'Beatriz Noheda' 'Stefan…
Maryada, Chiara De Luca, Arianna Rubino, Chenxi Wen + 4 more
Cortical microcircuits play a fundamental role in natural intelligence. While they inspired a wide range neural computation models and artificial intelligence algorithms, few attempts have been made to directly emulate them with an electronic computational substrate that uses the same physics of computation. Here we…
Michael L. Helde, Alexander G. Dimitrov
We adapted an olfactory neuromorphic algorithm to image and sound recognition. To achieve this, we carried out specific preprocessing procedures that were tailored to each modality. For images, we used the NIST digits dataset directly. For sound, we used samples from the Google Speech Command dataset. A gammatone…
Francesca Borghi, Thierry R. Nieus, Davide E. Galli, Paolo Milani
The brain’s ability to perform efficient and fault-tolerant data processing is strongly related to its peculiar interconnected adaptive architecture, based on redundant neural circuits interacting at different scales. By emulating the brain’s processing and learning mechanisms, computing technologies strive to achieve…
Elisabetta Chicca, Giacomo Indiveri
The development of memristive device technologies has reached a level of maturity to enable the design of complex and large-scale hybrid memristive-CMOS neural processing systems. These systems offer promising solutions for implementing novel in-memory computing architectures for machine learning and data analysis…
Gianvito Urgese, Antonio Rios-Navarro, Alejandro Linares-Barranco, Terrence C. Stewart + 1 more
The brain, this 3-pound mass of tissue that can easily be held in one's palm, has an inherent computational complexity that has always inspired efforts to endorse machines with some of its remarkable characteristics. Ironically, the brain computes in its own way, compared to analog or digital computers, despite sharing…
Dmitrii Zendrikov, Sergio Solinas, Giacomo Indiveri
Neuromorphic processing systems implementing spiking neural networks with mixed signal analog/digital electronic circuits and/or memristive devices represent a promising technology for edge computing applications that require low power, low latency, and that cannot connect to the cloud for off-line processing, either…
Danijela Markovic, Alice Mizrahi, Damien Querlioz, Julie Grollier
Neuromorphic computing takes inspiration from the brain to create energy efficient hardware for information processing, capable of highly sophisticated tasks. In this article, we make the case that building this new hardware necessitates reinventing electronics. We show that research in physics and material science…
Maryada, Saray Soldado-Magraner, Martino Sorbaro, Rodrigo Laje + 2 more
Many neural computations emerge from self-sustained patterns of activity in recurrent neural circuits, which rely on balanced excitation and inhibition. Neuromorphic electronic circuits that use the physics of silicon to emulate neuronal dynamics represent a promising approach for implementing the brain’s computational…
Saber Moradi, Rajit Manohar
Emergent nanoscale non-volatile memory technologies with high integration density offer a promising solution to overcome the scalability limitations of CMOS-based neural networks architectures, by efficiently exhibiting the key principle of neural computation. Despite the potential improvements in computational costs…
Dmitry Ivanov, Aleksandr Chezhegov, Andrey Grunin, М. Г. Киселев + 1 more
'Denis Larionov'] Modern AI systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the brain. This article discusses such limitations and the ways they can be mitigated. Next, it presents an overview of currently available neuromorphic AI…
Hongwei Cai, Zheng Ao, Chunhui Tian, Zhuhao Wu + 5 more
Brain-inspired hardware emulates the structure and working principles of a biological brain and may address the hardware bottleneck for fast-growing artificial intelligence (AI). Current brain-inspired silicon chips are promising but still limit their power to fully mimic brain function for AI computing. Here, we…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Authors not listed
A memristor is a two-terminal electronic component that modify its conductance state depending on how much charge has passed through it previously. Halide perovskites are materials recently employed for neuromorphic computing in this type of resistive switches. Their performance is rapidly improving, yet the activation…