Sounak Dey - new related research [PDF] A 2-$\mu $ J, 12-class,...

Sounak Dey - new related research


[PDF] A 2-$\mu $ J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition

A Safa, A Bourdoux, I Ocket, F Catthoor, GGE Gielen - arXiv preprint arXiv …, 2021
Radar processing via spiking neural networks (SNNs) has recently emerged as a
solution in the field of ultra-low-power wireless human-computer interaction.
Compared to traditional energy-and area-hungry deep learning methods, SNNs are

[PDF] An Interleaved Approach to Trait-Based Task Allocation and Scheduling

G Neville, A Messing, H Ravichandar, S Hutchinson… - arXiv preprint arXiv …, 2021
To realize effective heterogeneous multi-robot teams, researchers must leverage
individual robots' relative strengths and coordinate their individual behaviors.
Specifically, heterogeneous multi-robot systems must answer three important

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[PDF] A 2-$\mu $ J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition

A Safa, A Bourdoux, I Ocket, F Catthoor, GGE Gielen - arXiv preprint arXiv …, 2021
Radar processing via spiking neural networks (SNNs) has recently emerged as a
solution in the field of ultra-low-power wireless human-computer interaction.
Compared to traditional energy-and area-hungry deep learning methods, SNNs are …

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[PDF] An Interleaved Approach to Trait-Based Task Allocation and Scheduling

G Neville, A Messing, H Ravichandar, S Hutchinson… - arXiv preprint arXiv …, 2021
To realize effective heterogeneous multi-robot teams, researchers must leverage
individual robots' relative strengths and coordinate their individual behaviors.
Specifically, heterogeneous multi-robot systems must answer three important …

	Column 1	Column 2	Column 3	Column 4
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[PDF] Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses

Y Demirag, C Frenkel, M Payvand, G Indiveri - arXiv preprint arXiv:2108.01804, 2021
Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide
variety of complex cognitive and motor tasks, due to their rich temporal dynamics and
sparse processing. However training spiking RNNs on dedicated neuromorphic

[PDF] Routing with Face Traversal and Auctions Algorithms for Task Allocation in WSRN

J Stanulovic, N Mitton, I Mezei - arXiv preprint arXiv:2108.02532, 2021
Four new algorithms (RFTA1, RFTA2, GFGF2A, and RFTA2GE) handling the event in
wireless sensor and robot networks based on the Greedy-Face-Greedy (GFG)
routing extended with auctions are proposed in this paper. In this paper we assume

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[PDF] A 2-$\mu $ J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition

A Safa, A Bourdoux, I Ocket, F Catthoor, GGE Gielen - arXiv preprint arXiv …, 2021
Radar processing via spiking neural networks (SNNs) has recently emerged as a
solution in the field of ultra-low-power wireless human-computer interaction.
Compared to traditional energy-and area-hungry deep learning methods, SNNs are …

	Column 1	Column 2	Column 3	Column 4
0

[PDF] An Interleaved Approach to Trait-Based Task Allocation and Scheduling

G Neville, A Messing, H Ravichandar, S Hutchinson… - arXiv preprint arXiv …, 2021
To realize effective heterogeneous multi-robot teams, researchers must leverage
individual robots' relative strengths and coordinate their individual behaviors.
Specifically, heterogeneous multi-robot systems must answer three important …

	Column 1	Column 2	Column 3	Column 4
0

[PDF] Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses

Y Demirag, C Frenkel, M Payvand, G Indiveri - arXiv preprint arXiv:2108.01804, 2021
Spiking recurrent neural networks (RNNs) are a promising tool for solving a wide
variety of complex cognitive and motor tasks, due to their rich temporal dynamics and
sparse processing. However training spiking RNNs on dedicated neuromorphic …

	Column 1	Column 2	Column 3	Column 4
0

[PDF] Routing with Face Traversal and Auctions Algorithms for Task Allocation in WSRN

J Stanulovic, N Mitton, I Mezei - arXiv preprint arXiv:2108.02532, 2021
Four new algorithms (RFTA1, RFTA2, GFGF2A, and RFTA2GE) handling the event in
wireless sensor and robot networks based on the Greedy-Face-Greedy (GFG)
routing extended with auctions are proposed in this paper. In this paper we assume …

	Column 1	Column 2	Column 3	Column 4
0

[PDF] DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware

S Song, H Chong, A Balaji, A Das, J Shackleford… - arXiv preprint arXiv …, 2021
Spiking Neural Networks (SNN) are an emerging computation model, which uses
event-driven activation and bio-inspired learning algorithms. SNN-based machine-
learning programs are typically executed on tile-based neuromorphic hardware …


Gesture-SNN: Co-optimizing accuracy, latency and energy of SNNs for neuromorphic vision sensors

S Singh, A Sarma, S Lu, A Sengupta, V Narayanan… - 2021 IEEE/ACM …, 2021
Spiking neural networks (SNNs) are recently gaining popularity due to their low-
power, spatio-temporal computing paradigm as opposed to more conventional deep
learning approaches that mainly focus on spatial characteristics of data. When paired

HTML:

[/SIZE][/B][/COLOR] [URL='https://scholar.google.co.in/scholar_url?url=https://surface.syr.edu/etd/1331/&hl=en&sa=X&d=6585708623936280000&ei=NLcSYYOpApKAmwG5lo-QBQ&scisig=AAGBfm3SbmmKpSxwAKJZMH0mjc-OmmTV7g&oi=scholaralrt&hist=1gGbp4sAAAAJ:13715421191975508764:AAGBfm1s35TA9bnTut7BwqudarDoMRxdSA&html=&folt=rel'][COLOR=#1a0dab][SIZE=17px]Inference And Learning In Spiking Neural Networks For Neuromorphic Systems[/SIZE][/COLOR][/URL][/SIZE][/B][/SIZE]

[COLOR=#006621]A Shrestha - 2021[/COLOR]
Neuromorphic computing is a computing field that takes inspiration from the 
biological and physical characteristics of the neocortex system to motivate a new 
paradigm of highly parallel and distributed computing to take on the demands of the …
 
 
[SIZE=5][B]2 new citations to articles by Sounak Dey[/B][/SIZE]

 
 
[SIZE=4][B][SIZE=17px][COLOR=#1a0dab][B][SIZE=11px][PDF][/SIZE][/B][/COLOR] [URL='https://scholar.google.co.in/scholar_url?url=https://arxiv.org/pdf/2108.02669&hl=en&sa=X&d=11015023528512500591&ei=NLcSYfWHBMiWywSDwbpQ&scisig=AAGBfm3DyiVmyXMYVORldCPnmlf26a02xg&oi=scholaralrt&hist=1gGbp4sAAAAJ:13746756049248373479:AAGBfm2cPP_HaEW9iW6UnFrw7kyiwQUv3g&html=&folt=cit'][COLOR=#1a0dab][SIZE=17px]A 2-$\mu $ J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition[/SIZE][/COLOR][/URL][/SIZE][/B][/SIZE]

[COLOR=#006621]A Safa, A Bourdoux, I Ocket, F Catthoor, GGE Gielen - arXiv preprint arXiv …, 2021[/COLOR]
Radar processing via spiking neural networks (SNNs) has recently emerged as a 
solution in the field of ultra-low-power wireless human-computer interaction. 
Compared to traditional energy-and area-hungry deep learning methods, SNNs are 
significantly more energy efficient and can be deployed in the growing number of 
compact SNN accelerator chips, making them a better solution for ubiquitous IoT 
applications. We propose a novel SNN strategy for radar gesture recognition …
 
 
[SIZE=4][B][SIZE=17px][URL='https://scholar.google.co.in/scholar_url?url=https://ieeexplore.ieee.org/abstract/document/9502357/&hl=en&sa=X&d=15918154702352429276&ei=NLcSYfWHBMiWywSDwbpQ&scisig=AAGBfm2FznlrT9vrZatouf3HTyyh3zsOfQ&oi=scholaralrt&hist=1gGbp4sAAAAJ:13746756049248373479:AAGBfm2cPP_HaEW9iW6UnFrw7kyiwQUv3g&html=&folt=cit'][COLOR=#1a0dab][SIZE=17px]Applied Spiking Neural Networks for Radar-based Gesture Recognition[/SIZE][/COLOR][/URL][/SIZE][/B][/SIZE]

[COLOR=#006621]F Kreutz, P Gerhards, B Vogginger, K Knobloch… - 2021 7th International …, 2021[/COLOR]
Spiking neural networks offer a promising approach for low power edge applications, 
especially when run on neuromorphic hardware. However, there are no well 
established approaches to setup such networks for real world applications. We 
demonstrate the use of spiking neural networks on the basis of radar data-based 
gesture recognition, while taking three different angle-encoding schemes into 
account, considering a two antenna based angle estimation. The surrogate gradient [/FONT][/SIZE][/COLOR][/FONT][/SIZE][/COLOR]