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 …
Metasurface on integrated photonic platform: from mode converters to machine learning
Integrated photonic circuits are created as a stable and small form factor analogue of fiber-
based optical systems, from wavelength-division multiplication transceivers to more recent …
based optical systems, from wavelength-division multiplication transceivers to more recent …
BiCoSS: toward large-scale cognition brain with multigranular neuromorphic architecture
The further exploration of the neural mechanisms underlying the biological activities of the
human brain depends on the development of large-scale spiking neural networks (SNNs) …
human brain depends on the development of large-scale spiking neural networks (SNNs) …
An Always-On 3.8 J/86% CIFAR-10 Mixed-Signal Binary CNN Processor With All Memory on Chip in 28-nm CMOS
The trend of pushing inference from cloud to edge due to concerns of latency, bandwidth,
and privacy has created demand for energy-efficient neural network hardware. This paper …
and privacy has created demand for energy-efficient neural network hardware. This paper …
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 …
MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor With Stochastic Spike-Driven Online Learning
Recent trends in the field of neural network accelerators investigate weight quantization as a
means to increase the resourceand power-efficiency of hardware devices. As full on-chip …
means to increase the resourceand power-efficiency of hardware devices. As full on-chip …
DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor
O Richter, C Wu, AM Whatley, G Köstinger… - Neuromorphic …, 2024 - iopscience.iop.org
With the remarkable progress that technology has made, the need for processing data near
the sensors at the edge has increased dramatically. The electronic systems used in these …
the sensors at the edge has increased dramatically. The electronic systems used in these …
Nadol: Neuromorphic architecture for spike-driven online learning by dendrites
S Yang, H Wang, Y Pang, MR Azghadi… - … Circuits and Systems, 2023 - ieeexplore.ieee.org
Biologically plausible learning with neuronal dendrites is a promising perspective to improve
the spike-driven learning capability by introducing dendritic processing as an additional …
the spike-driven learning capability by introducing dendritic processing as an additional …
Bottom-up and top-down neural processing systems design: Neuromorphic intelligence as the convergence of natural and artificial intelligence
While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …
calls for new avenues for improving the overall system performance. One of these avenues …
DNN engine: A 28-nm timing-error tolerant sparse deep neural network processor for IoT applications
This paper presents a 28-nm system-on-chip (SoC) for Internet of things (IoT) applications
with a programmable accelerator design that implements a powerful fully connected deep …
with a programmable accelerator design that implements a powerful fully connected deep …