Filters








36 Hits in 4.5 sec

Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Framework [article]

Zhuowen Zou, Haleh Alimohamadi, Farhad Imani, Yeseong Kim, Mohsen Imani
2021 arXiv   pre-print
With the help of the classical psychological model on memory, we propose SpikeHD, the first framework that fundamentally combines Spiking neural network and hyperdimensional computing.  ...  Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning.  ...  Supplementary material for "Spiking Hyperdimensional Network: Neuromorphic Models Integrated with Memory-Inspired Framework" Hyperdimensional Classification We present a robust and lightweight hyperdimensional  ... 
arXiv:2110.00214v1 fatcat:kjaxx2xjzze7zbbl56h2bbwemu

Neuromorphic Visual Odometry with Resonator Networks [article]

Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, E. Paxon Frady, Friedrich T. Sommer, Yulia Sandamirskaya
2022 arXiv   pre-print
The VO network we propose generates and stores a working memory of the presented visual environment.  ...  However, VO with conventional cameras is computationally demanding, limiting its application in systems with strict low-latency, -memory, and -energy requirements.  ...  model, implemented, ran, and analyzed the network simulations; LS ran the robotic arm experiments.  ... 
arXiv:2209.02000v1 fatcat:5aqzlg72urdrrl2gdxfwaiw3da

Neuromorphic Visual Scene Understanding with Resonator Networks [article]

Alpha Renner, Lazar Supic, Andreea Danielescu, Giacomo Indiveri, Bruno A. Olshausen, Yulia Sandamirskaya, Friedrich T. Sommer, E. Paxon Frady
2022 arXiv   pre-print
The spiking neuron model allows to map the resonator network onto efficient and low-power neuromorphic hardware.  ...  ; (3) the design of a multi-compartment spiking phasor neuron model for implementing complex-valued vector binding on neuromorphic hardware.  ...  We thank Intel Neuromorphic Computing Lab for providing access to the Loihi hardware and related software. We thank Elvin Hajizada for running CPU power measurements.  ... 
arXiv:2208.12880v1 fatcat:3nc46ya5cbdp5e6sxvy27plft4

A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks [article]

Jibin Wu, Yansong Chua, Malu Zhang, Guoqi Li, Haizhou Li, Kay Chen Tan
2020 arXiv   pre-print
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.  ...  In this work, we propose a tandem learning framework, that consists of an SNN and an Artificial Neural Network (ANN) coupled through weight sharing.  ...  integrate-and-fire (LIF) neuron models [33] .  ... 
arXiv:1907.01167v3 fatcat:zwfeidsnkzh23pv5hq6oyrfecy

Memristors – from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing [article]

Adnan Mehonic, Abu Sebastian, Bipin Rajendran, Osvaldo Simeone, Eleni Vasilaki, Anthony J. Kenyon
2020 arXiv   pre-print
spiking neural networks.  ...  Deep learning is based on computational models that are, to a certain extent, bio-inspired, as they rely on networks of connected simple computing units operating in parallel.  ...  SNNs are typically modelled on the integrate-and-fire behaviour of neurons in the brain. In this framework, neurons communicate with each other using binary signals or spikes.  ... 
arXiv:2004.14942v1 fatcat:b52hrjk365f2tabarxg4zfys44

Photonic Neural Networks: a Survey

Lorenzo De Marinis, Marco Cococcioni, Piero Castoldi, Nicola Andriolli
2019 IEEE Access  
We propose a taxonomy of the existing solutions (categorized into multilayer perceptrons, convolutional neural networks, spiking neural networks, and reservoir computing) with emphasis on proof-of-concept  ...  INDEX TERMS Artificial neural networks, neural network hardware, photonics, neuromorphic computing, photonic neural networks.  ...  This sparse topology is inspired to receptive fields found in biological systems. Both (a) and (b) are stateless networks, i.e., networks without memory.  ... 
doi:10.1109/access.2019.2957245 fatcat:2ydkl3s3pnafnp23mzhjzb3iui

Progress and Challenges of Neuroscience and Brain-inspired Artificial Intelligence

Lidong Wang, Cheryl Ann Alexander
2019 Neuroscience International  
graph, brain networks, the connectome, brain reconstruction, imaging technologies used for the brain, chips and devices inspired by the human brain, brain-computer interface or brain-machine interfaces  ...  Neuroscience and brain-inspired artificial intelligence are significant research areas.  ...  Brain-inspired hyperdimensional (HD) computing emulates cognition tasks by computing with hypervectors.  ... 
doi:10.3844/amjnsp.2019.13.21 fatcat:dtoa5moa2bhwdbiaxe6belwlf4

Progress and Challenges of Neuroscience and Brain-inspired Artificial Intelligence

Lidong Wang, Cheryl Ann Alexander
2020 Neuroscience International  
graph, brain networks, the connectome, brain reconstruction, imaging technologies used for the brain, chips and devices inspired by the human brain, brain-computer interface or brain-machine interfaces  ...  Neuroscience and brain-inspired artificial intelligence are significant research areas.  ...  Brain-inspired hyperdimensional (HD) computing emulates cognition tasks by computing with hypervectors.  ... 
doi:10.3844/amjnsp.2020.1.9 fatcat:apbgz22m6ffwzehijvq662pvwe

2022 Roadmap on Neuromorphic Computing and Engineering [article]

Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco, Abu Sebastian, Manuel Le Gallo, Andrea Redaelli, Stefan Slesazeck, Thomas Mikolajick, Sabina Spiga, Stephan Menzel, Ilia Valov, Gianluca Milano (+47 others)
2022 arXiv   pre-print
These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain.  ...  In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously.  ...  Concluding Remarks Integrating event-based vision sensing and processing with neuromorphic computation techniques is expected to yield solutions that will be able to penetrate the artificial vision market  ... 
arXiv:2105.05956v3 fatcat:pqir5infojfpvdzdwgmwdhsdi4

Recent advances in physical reservoir computing: A review

Gouhei Tanaka, Toshiyuki Yamane, Jean Benoit Héroux, Ryosho Nakane, Naoki Kanazawa, Seiji Takeda, Hidetoshi Numata, Daiju Nakano, Akira Hirose
2019 Neural Networks  
It is derived from several recurrent neural network models, including echo state networks and liquid state machines.  ...  Reservoir computing is a computational framework suited for temporal/sequential data processing.  ...  Multiple working memory models based on RC and their variants have been proposed, including the working memory model with generic cortical microcircuit models with feedback , the ESN-based working memory  ... 
doi:10.1016/j.neunet.2019.03.005 fatcat:u4vjykpyxnch3n24kprqoi2tsy

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning

Prathyush Poduval, Haleh Alimohamadi, Ali Zakeri, Farhad Imani, M. Hassan Najafi, Tony Givargis, Mohsen Imani
2022 Frontiers in Neuroscience  
Brain-inspired HyperDimensional Computing (HDC) is introduced as a model of human memory.  ...  Therefore, it mimics several important functionalities of the brain memory by operating with a vector that is computationally tractable and mathematically rigorous in describing human cognition.  ...  Besides SPAUN, as vector symbolic architecture, GrapHD has full compatibility with the new Intel neuromorphic framework, i.e., LAVA.  ... 
doi:10.3389/fnins.2022.757125 pmid:35185456 pmcid:PMC8855686 fatcat:kknxv5wnajaqnhg3b2hgndfoje

A Construction Kit for Efficient Low Power Neural Network Accelerator Designs [article]

Petar Jokic, Erfan Azarkhish, Andrea Bonetti, Marc Pons, Stephane Emery, Luca Benini
2021 arXiv   pre-print
Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption.  ...  Reported optimizations range from up to 10'000x memory savings to 33x energy reductions, providing chip designers an overview of design choices for implementing efficient low power neural network accelerators  ...  185] . 2) Hyperdimensional computing Hyperdimensional computing is another brain-inspired computing approach that encodes information in very highdimensional binary vectors with thousands of entries,  ... 
arXiv:2106.12810v1 fatcat:gx7cspazc5fdfoi64t2zjth7am

An electronic neuromorphic system for real-time detection of high frequency oscillations (HFO) in intracranial EEG

Mohammadali Sharifshazileh, Karla Burelo, Johannes Sarnthein, Giacomo Indiveri
2021 Nature Communications  
Here we present a neuromorphic system that combines a neural recording headstage with a spiking neural network (SNN) processing core on the same die for processing iEEG, and show how it can reliably detect  ...  Mixed-signal neuromorphic circuits offer the possibility of building compact and low-power neural network processing systems that can analyze data on-line in real-time.  ...  Whatley, for developing the software and firmware required to communicate with the hardware platform and for configuring the parameters on chip.  ... 
doi:10.1038/s41467-021-23342-2 pmid:34035249 fatcat:diqxwvvqjrcnphq77m6byj53hi

Nonsilicon, Non-von Neumann Computing—Part I [Scanning the Issue]

Sankar Basu, Randal E. Bryant, Giovanni De Micheli, Thomas Theis, Lloyd Whitman
2019 Proceedings of the IEEE  
Transmitting, storing, processing, and analyzing this data explosion with the requisite speed and performance-and enabling significant processing and analysis to occur locally or at network nodes (i.e.  ...  The need to implement deep learning algorithms with energy-efficient hardware has in turn spiked interest in neuromorphic hardware.  ...  The experience of the research team at IBM is elaborated by bringing in in-memory compute paradigm, and the possibilities of integrating analog circuitry into deep learning networks.  ... 
doi:10.1109/jproc.2018.2884780 fatcat:nzx7jkv3izdufhirs5xqspd6uq

Analogical mapping and inference with binary spatter codes and sparse distributed memory

Blerim Emruli, Ross W. Gayler, Fredrik Sandin
2013 The 2013 International Joint Conference on Neural Networks (IJCNN)  
We study a novel VSA network for the analogical mapping of compositional structures, which integrates an associative memory known as sparse distributed memory (SDM).  ...  Analogies require complex, relational representations of learned structures, which is challenging for both symbolic and neurally inspired models.  ...  VSAs may be treated as mathematical abstractions and implemented directly as connectionist models with limited biological realism [6] , or implemented more realistically with networks of spiking neurons  ... 
doi:10.1109/ijcnn.2013.6706829 dblp:conf/ijcnn/EmruliGS13 fatcat:bm34lczbhbaebobtu2vlz5bxyq
« Previous Showing results 1 — 15 out of 36 results