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Found 10 papers, 1 papers with code
A Review on Digital Pixel Sensors
A comparative study based on different performance metrics has also been showcased for a well-rounded understanding.
Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology
Neuromorphic computing and, in particular, spiking neural networks (SNNs) have become an attractive alternative to deep neural networks for a broad range of signal processing applications, processing static and/or temporal inputs from different sensory modalities, including audio and vision sensors.
Object Motion Sensitivity: A Bio-inspired Solution to the Ego-motion Problem for Event-based Cameras
Specifically, the sensory information generated by event-based image sensors are orders of magnitude sparser compared to that of RGB sensors.
IRIS: Integrated Retinal Functionality in Image Sensors
Neuromorphic image sensors draw inspiration from the biological retina to implement visual computations in electronic hardware.
Intrinsic Spike Timing Dependent Plasticity in Stochastic Magnetic Tunnel Junctions Mediated by Heat Dynamics
Neuromorphic computing aims to mimic the behavior of biological neurons and synapses using solid-state devices and circuits.
IMAC: In-memory multi-bit Multiplication andACcumulation in 6T SRAM Array
`In-memory computing' is being widely explored as a novel computing paradigm to mitigate the well known memory bottleneck.
Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays
In this paper, we demonstrate how deep binary networks can be accelerated in modified von-Neumann machines by enabling binary convolutions within the SRAM array.
Emerging Technologies
Voltage-Driven Domain-Wall Motion based Neuro-Synaptic Devices for Dynamic On-line Learning
The basic building blocks of such neuromorphic systems are neurons and synapses.
Proposal for a Leaky-Integrate-Fire Spiking Neuron based on Magneto-Electric Switching of Ferro-magnets
The efficiency of the human brain in performing classification tasks has attracted considerable research interest in brain-inspired neuromorphic computing.
Significance Driven Hybrid 8T-6T SRAM for Energy-Efficient Synaptic Storage in Artificial Neural Networks
Our analysis on a widely used digit recognition dataset indicates that the voltage can be scaled by 200mV from the nominal operating voltage (950mV) for practically no loss (less than 0. 5%) in accuracy (22nm predictive technology).
