Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware
Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …
attention lately due to its promise of reducing the computational energy, latency, as well as …
Spiking neural network integrated circuits: A review of trends and future directions
The rapid growth of deep learning, spurred by its successes in various fields ranging from
face recognition [1] to game playing [2], has also triggered a growing interest in the design of …
face recognition [1] to game playing [2], has also triggered a growing interest in the design of …
Fusion-FlowNet: Energy-efficient optical flow estimation using sensor fusion and deep fused spiking-analog network architectures
Standard frame-based cameras that sample light intensity frames are heavily impacted by
motion blur for high-speed motion and fail to perceive scene accurately in high-dynamic …
motion blur for high-speed motion and fail to perceive scene accurately in high-dynamic …
Compute-in-memory technologies and architectures for deep learning workloads
The use of deep learning (DL) to real-world applications, such as computer vision, speech
recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous …
recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous …
A heterogeneous rram in-memory and sram near-memory soc for fused frame and event-based target identification and tracking
Accurate identification of the target and tracking it at high speeds using drone-mounted
cameras and compute hardware finds military and commercial applications. Conventional …
cameras and compute hardware finds military and commercial applications. Conventional …
Recent developments in low-power AI accelerators: A survey
As machine learning and AI continue to rapidly develop, and with the ever-closer end of
Moore's law, new avenues and novel ideas in architecture design are being created and …
Moore's law, new avenues and novel ideas in architecture design are being created and …
Hardware/software co-design with adc-less in-memory computing hardware for spiking neural networks
Spiking Neural Networks (SNNs) are bio-plausible models that hold great potential for
realizing energy-efficient implementations of sequential tasks on resource-constrained edge …
realizing energy-efficient implementations of sequential tasks on resource-constrained edge …
A 18.7 TOPS/W mixed-signal spiking neural network processor with 8-bit synaptic weight on-chip learning that operates in the continuous-time domain
S Uenohara, K Aihara - IEEE Access, 2022 - ieeexplore.ieee.org
We present a mixed-signal spiking neural networks processor with 8-bit synaptic weight on-
chip learning in 40 nm CMOS that consists of a 10k mixed-signal synapse circuit and 100 …
chip learning in 40 nm CMOS that consists of a 10k mixed-signal synapse circuit and 100 …
A low-power, high-accuracy with fully on-chip ternary weight hardware architecture for Deep Spiking Neural Networks
Abstract Recently, Deep Spiking Neural Network (DSNN) has emerged as a promising
neuromorphic approach for various AI-based applications, such as image classification …
neuromorphic approach for various AI-based applications, such as image classification …
PAICORE: A 1.9-Million-Neuron 5.181-TSOPS/W Digital Neuromorphic Processor With Unified SNN-ANN and On-Chip Learning Paradigm
Y Zhong, Y Kuang, K Liu, Z Wang… - IEEE Journal of Solid …, 2024 - ieeexplore.ieee.org
The neuromorphic approach of fulfilling brain-like edge intelligence is confronted with three
paramount challenges: 1) ever-increasing application demands versus insufficient on-chip …
paramount challenges: 1) ever-increasing application demands versus insufficient on-chip …