659 citations · 813 across the 15 of their papers we have counts for
17 papers
Efficient 3D Recognition with Event-driven Spike Sparse Convolution
Xuerui Qiu, Man Yao, Jieyuan Zhang +5
Spiking Neural Networks (SNNs) provide an energy-efficient way to extract 3D spatio-temporal features. Point clouds are sparse 3D spatial data, which suggests that SNNs should be w…
Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor
Ruixin Lia, Guoxu Zhaoa, Dylan Richard Muir +6
Analyzing electroencephalogram (EEG) signals to detect the epileptic seizure status of a subject presents a challenge to existing technologies aimed at providing timely and efficie…
High-Performance Temporal Reversible Spiking Neural Networks with Training Memory and Inference Cost
JiaKui Hu, Man Yao, Xuerui Qiu +6
Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods ca…
Vector-Symbolic Architecture for Event-Based Optical Flow
Hongzhi You, Yijun Cao, Wei Yuan +3
From a perspective of feature matching, optical flow estimation for event cameras involves identifying event correspondences by comparing feature similarity across accompanying eve…
DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor
Ole Richter, Chenxi Wu, Adrian M. Whatley +4
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 ap…
"Seeing'' Electric Network Frequency from Events
Lexuan Xu, Guang Hua, Haijian Zhang +2
Most of the artificial lights fluctuate in response to the grid's alternating current and exhibit subtle variations in terms of both intensity and spectrum, providing the potential…
Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence
Arianna Rubino, Can Livanelioglu, Ning Qiao +2
Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications. In-memory computing mixed-signal n…
Automatic gain control of ultra-low leakage synaptic scaling homeostatic plasticity circuits
Ning Qiao, Giacomo Indiveri, Chiara Bartolozzi
Homeostatic plasticity is a stabilizing mechanism that allows neural systems to maintain their activity around a functional operating point. This is an extremely useful mechanism f…
Scaling mixed-signal neuromorphic processors to 28 nm FD-SOI technologies
Ning Qiao, Giacomo Inidveri
As processes continue to scale aggressively, the design of deep sub-micron, mixed-signal design is becoming more and more challenging. In this paper we present an analysis of scali…
An auto-scaling wide dynamic range current to frequency converter for real-time monitoring of signals in neuromorphic systems
Ning Qiao, Giacomo Indiveri
Neuromorphic systems typically employ current-mode circuits that model neural dynamics and produce output currents that range from few pico-Amperes to hundreds of micro-Amperes. On…
Analog circuits for mixed-signal neuromorphic computing architectures in 28 nm FD-SOI technology
Ning Qiao, Giacomo Indiveri
Developing mixed-signal analog-digital neuromorphic circuits in advanced scaled processes poses significant design challenges. We present compact and energy efficient sub-threshold…
A bi-directional Address-Event transceiver block for low-latency inter-chip communication in neuromorphic systems
Ning Qiao, Giacomo Indiveri
Neuromorphic systems typically use the Address-Event Representation (AER) to transmit signals among nodes, cores, and chips. Communication of Address-Events (AEs) between neuromorp…
A clock-less ultra-low power bit-serial LVDS link for Address-Event multi-chip systems
Ning Qiao, Giacomo Indiveri
We present a power efficient clock-less fully asynchronous bit-serial Low Voltage Differential Signaling (LVDS) link with event-driven instant wake-up and self-sleep features, opti…
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor
Sebastian Glatz, Julien N. P. Martel, Raphaela Kreiser +2
Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent pa…
Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain
Chetan Singh Thakur, Jamal Molin, Gert Cauwenberghs +12
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuro…
An Ultralow Leakage Synaptic Scaling Homeostatic Plasticity Circuit With Configurable Time Scales up to 100 ks
Ning Qiao, Chiara Bartolozzi, Giacomo Indiveri
Homeostatic plasticity is a stabilizing mechanism commonly observed in real neural systems that allows neurons to maintain their activity around a functional operating point. This…
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Saber Moradi, Ning Qiao, Fabio Stefanini +1
Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantag…