Gas recognition in E-nose system: A review
Gas recognition is essential in an electronic nose (E-nose) system, which is responsible for
recognizing multivariate responses obtained by gas sensors in various applications. Over …
recognizing multivariate responses obtained by gas sensors in various applications. Over …
Spiking neural networks hardware implementations and challenges: A survey
M Bouvier, A Valentian, T Mesquida… - ACM Journal on …, 2019 - dl.acm.org
Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …
Research progress on low-power artificial intelligence of things (AIoT) chip design
An artificial intelligence of things (AIoT) chip is a critical hardware component in edge
devices that supports data acquisition and processing in the artificial intelligence (AI) era. In …
devices that supports data acquisition and processing in the artificial intelligence (AI) era. In …
Colonnade: A reconfigurable SRAM-based digital bit-serial compute-in-memory macro for processing neural networks
This article (Colonnade) presents a fully digital bit-serial compute-in-memory (CIM) macro.
The digital CIM macro is designed for processing neural networks with reconfigurable 1-16 …
The digital CIM macro is designed for processing neural networks with reconfigurable 1-16 …
μBrain: An event-driven and fully synthesizable architecture for spiking neural networks
The development of brain-inspired neuromorphic computing architectures as a paradigm for
Artificial Intelligence (AI) at the edge is a candidate solution that can meet strict energy and …
Artificial Intelligence (AI) at the edge is a candidate solution that can meet strict energy and …
ReckOn: A 28nm sub-mm2 task-agnostic spiking recurrent neural network processor enabling on-chip learning over second-long timescales
C Frenkel, G Indiveri - 2022 IEEE International Solid-State …, 2022 - ieeexplore.ieee.org
The robustness of autonomous inference-only devices deployed in the real world is limited
by data distribution changes induced by different users, environments, and task …
by data distribution changes induced by different users, environments, and task …
Mixed-precision deep learning based on computational memory
Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have
achieved unprecedented success in cognitive tasks such as image and speech recognition …
achieved unprecedented success in cognitive tasks such as image and speech recognition …
ACE-SNN: Algorithm-hardware co-design of energy-efficient & low-latency deep spiking neural networks for 3d image recognition
High-quality 3D image recognition is an important component of many vision and robotics
systems. However, the accurate processing of these images requires the use of compute …
systems. However, the accurate processing of these images requires the use of compute …
A 73.53 TOPS/W 14.74 TOPS heterogeneous RRAM in-memory and SRAM near-memory SoC for hybrid frame and event-based target tracking
Vision-based high-speed target-identification and tracking is a critical application in
unmanned aerial vehicles (UAV) with wide military and commercial usage. Traditional frame …
unmanned aerial vehicles (UAV) with wide military and commercial usage. Traditional frame …
Photonic spiking neural networks with event-driven femtojoule optoelectronic neurons based on Izhikevich-inspired model
Photonic spiking neural networks (PSNNs) potentially offer exceptionally high throughput
and energy efficiency compared to their electronic neuromorphic counterparts while …
and energy efficiency compared to their electronic neuromorphic counterparts while …