Search Results for author:
Found 33 papers, 4 papers with code
Memory Efficient Corner Detection for Event-driven Dynamic Vision Sensors
In this paper, we propose a new event-driven corner detection implementation tailored for edge computing devices, which requires much lower memory access than luvHarris while also improving accuracy.
ANN vs SNN: A case study for Neural Decoding in Implantable Brain-Machine Interfaces
While it is important to make implantable brain-machine interfaces (iBMI) wireless to increase patient comfort and safety, the trend of increased channel count in recent neural probes poses a challenge due to the concomitant increase in the data rate.
Towards Neuromorphic Compression based Neural Sensing for Next-Generation Wireless Implantable Brain Machine Interface
Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection.
An Efficient Hash-based Data Structure for Dynamic Vision Sensors and its Application to Low-energy Low-memory Noise Filtering
Events generated by the Dynamic Vision Sensor (DVS) are generally stored and processed in two-dimensional data structures whose memory complexity and energy-per-event scale proportionately with increasing sensor dimensions.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Tracking Fast by Learning Slow: An Event-based Speed Adaptive Hand Tracker Leveraging Knowledge in RGB Domain
To realize our solution, we constructed the first 3D hand tracking dataset captured by an event camera in a real-world environment, figured out two data augment methods to narrow the domain gap between slow and fast motion data, developed a speed adaptive event stream segmentation method to handle hand movements in different moving speeds, and introduced a new event-to-frame representation method adaptive to event streams with different lengths.
Intelligence Processing Units Accelerate Neuromorphic Learning
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads.
Spiking Neural Network Integrated Circuits: A Review of Trends and Future Directions
In this paper, we reviewed Spiking neural network (SNN) integrated circuit designs and analyzed the trends among mixed-signal cores, fully digital cores and large-scale, multi-core designs.
A 51.3 TOPS/W, 134.4 GOPS In-memory Binary Image Filtering in 65nm CMOS
As compared to fully digital implementation, IMF enables > 70x energy savings and a > 3x improvement of processing time when tested with the video recordings from a DAVIS sensor and achieves a peak throughput of 134. 4 GOPS.
Prospects for Analog Circuits in Deep Networks
Emerging dense non-volatile memory technologies can help to provide on-chip memory and analog circuits can be well suited to implement the needed multiplication-vector operations coupled with in-computing memory approaches.
A $0.11-0.38$ pJ/cycle Differential Ring Oscillator in $65$ nm CMOS for Robust Neurocomputing
This paper presents a low-area and low-power consumption CMOS differential current controlled oscillator (CCO) for neuromorphic applications.
ADIC: Anomaly Detection Integrated Circuit in 65nm CMOS utilizing Approximate Computing
An additional 42% energy saving can be achieved when a lighter version of OPIUM method is used for training with the same number of data samples lead to no significant compromise on the quality of inference.
A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-time Systems
Deep learning inference that needs to largely take place on the 'edge' is a highly computational and memory intensive workload, making it intractable for low-power, embedded platforms such as mobile nodes and remote security applications.
EBBINNOT: A Hardware Efficient Hybrid Event-Frame Tracker for Stationary Dynamic Vision Sensors
To the best of our knowledge, this is the first time a stationary DVS based traffic monitoring solution is extensively compared to simultaneously recorded RGB frame-based methods while showing tremendous promise by outperforming state-of-the-art deep learning solutions.
Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning
We also implement a patient specific model tuning strategy that first screens respiratory patients and then builds patient specific classification models using limited patient data for reliable anomaly detection.
HyNNA: Improved Performance for Neuromorphic Vision Sensor based Surveillance using Hybrid Neural Network Architecture
Applications in the Internet of Video Things (IoVT) domain have very tight constraints with respect to power and area.
Is my Neural Network Neuromorphic? Taxonomy, Recent Trends and Future Directions in Neuromorphic Engineering
In this paper, we review recent work published over the last 3 years under the umbrella of Neuromorphic engineering to analyze what are the common features among such systems.
ADEPOS: A Novel Approximate Computing Framework for Anomaly Detection Systems and its Implementation in 65nm CMOS
However, on the detection of anomalies, the complexity of the network and computing precision are adaptively increased for accurate predictions.
A low-power end-to-end hybrid neuromorphic framework for surveillance applications
With the success of deep learning, object recognition systems that can be deployed for real-world applications are becoming commonplace.
EBBIOT: A Low-complexity Tracking Algorithm for Surveillance in IoVT Using Stationary Neuromorphic Vision Sensors
In this paper, we present EBBIOT-a novel paradigm for object tracking using stationary neuromorphic vision sensors in low-power sensor nodes for the Internet of Video Things (IoVT).
Spiking Neural Network based Region Proposal Networks for Neuromorphic Vision Sensors
This paper presents a three layer spiking neural network based region proposal network operating on data generated by neuromorphic vision sensors.
A Stacked Autoencoder Neural Network based Automated Feature Extraction Method for Anomaly detection in On-line Condition Monitoring
Most of these are handcrafted features, where these are manually obtained based on the nature of the raw data.
Power efficient Spiking Neural Network Classifier based on memristive crossbar network for spike sorting application
The advantage of the former classifier is that it is power efficient while providing comparable accuracy as that of the digital implementation due to the robustness of the SNN training algorithm which has a good tolerance for variation in memristance.
VLSI Extreme Learning Machine: A Design Space Exploration
In this paper, we describe a compact low-power, high performance hardware implementation of the extreme learning machine (ELM) for machine learning applications.
An Online Structural Plasticity Rule for Generating Better Reservoirs
The proposed learning rule is inspired from structural plasticity and trains the liquid through formation and elimination of synaptic connections.
Hardware Architecture for Large Parallel Array of Random Feature Extractors applied to Image Recognition
We demonstrate a low-power and compact hardware implementation of Random Feature Extractor (RFE) core.
An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks
To demonstrate the performance of the proposed network and learning rule, we employ it to solve two, four and six class classification of random Poisson spike time inputs.
Triplet Spike Time Dependent Plasticity: A floating-gate Implementation
Synapse plays an important role of learning in a neural network; the learning rules which modify the synaptic strength based on the timing difference between the pre- and post-synaptic spike occurrence is termed as Spike Time Dependent Plasticity (STDP).
A 128 channel Extreme Learning Machine based Neural Decoder for Brain Machine Interfaces
The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99. 3% for movement type.
Learning Spike time codes through Morphological Learning with Binary Synapses
In this paper, a neuron with nonlinear dendrites (NNLD) and binary synapses that is able to learn temporal features of spike input patterns is considered.
Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations
Compared to the parallel perceptron architecture trained by the p-delta algorithm, which is the state of the art in terms of performance of readout stages, our readout architecture and learning algorithm can attain better performance with significantly less synaptic resources making it attractive for VLSI implementation.
Hardware-Amenable Structural Learning for Spike-based Pattern Classification using a Simple Model of Active Dendrites
This work also presents a branch-specific spike-based version of this structural plasticity rule.
Delay Learning Architectures for Memory and Classification
We present a neuromorphic spiking neural network, the DELTRON, that can remember and store patterns by changing the delays of every connection as opposed to modifying the weights.
