Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference最新文献

Neuromorphic Downsampling of Event-Based Camera Output 基于事件的相机输出的神经形态下采样

Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference Pub Date : 2023-04-11 DOI: 10.1145/3584954.3584962

Charles Rizzo, C. Schuman, J. Plank

引用次数: 4

Translation and Scale Invariance for Event-Based Object tracking 基于事件的目标跟踪的平移和尺度不变性

Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference Pub Date : 2023-04-11 DOI: 10.1145/3584954.3584996

Jens Egholm Pedersen, Raghav Singhal, J. Conradt

引用次数: 0

Spiking LCA in a Neural Circuit with Dictionary Learning and Synaptic Normalization 基于字典学习和突触归一化的神经回路中的脉冲LCA

Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference Pub Date : 2023-04-11 DOI: 10.1145/3584954.3584968

Diego Chavez Arana, Alpha Renner, A. Sornborger

{"title":"Spiking LCA in a Neural Circuit with Dictionary Learning and Synaptic Normalization","authors":"Diego Chavez Arana, Alpha Renner, A. Sornborger","doi":"10.1145/3584954.3584968","DOIUrl":"https://doi.org/10.1145/3584954.3584968","url":null,"abstract":"The Locally Competitive Algorithm (LCA) [17, 18] was put forward as a model of primary visual cortex [14, 17] and has been used extensively as a sparse coding algorithm for multivariate data. LCA has seen implementations on neuromorphic processors, including IBM’s TrueNorth processor [10], and Intel’s neuromorphic research processor, Loihi, which show that it can be very efficient with respect to the power resources it consumes [8]. When combined with dictionary learning [13], the LCA algorithm encounters synaptic instability [24], where, as a synapse’s strength grows, its activity increases, further enhancing synaptic strength, leading to a runaway condition, where synapses become saturated [3, 15]. A number of approaches have been suggested to stabilize this phenomenon [1, 2, 5, 7, 12]. Previous work demonstrated that, by extending the cost function used to generate LCA updates, synaptic normalization could be achieved, eliminating synaptic runaway [7]. It was also shown that the resulting algorithm could be implemented in a firing rate model [7]. Here, we implement a probabilistic approximation to this firing rate model as a spiking LCA algorithm that includes dictionary learning and synaptic normalization. The algorithm is based on a synfire-gated synfire chain-based information control network in concert with Hebbian synapses [16, 19]. We show that this algorithm results in correct classification on numeric data taken from the MNIST dataset. LA-UR-22-33004","PeriodicalId":375527,"journal":{"name":"Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132873242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Demonstration of neuromorphic sequence learning on a memristive array 记忆阵列上神经形态序列学习的演示

Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference Pub Date : 2023-04-11 DOI: 10.1145/3584954.3585000

Sebastian Siegel, Tobias Ziegler, Younes Bouhadjar, T. Tetzlaff, R. Waser, R. Dittmann, D. Wouters

{"title":"Demonstration of neuromorphic sequence learning on a memristive array","authors":"Sebastian Siegel, Tobias Ziegler, Younes Bouhadjar, T. Tetzlaff, R. Waser, R. Dittmann, D. Wouters","doi":"10.1145/3584954.3585000","DOIUrl":"https://doi.org/10.1145/3584954.3585000","url":null,"abstract":"Sequence learning and prediction are considered principle computations performed by biological brains. Machine learning algorithms solve this type of task, but they require large amounts of training data and a substantial energy budget. An approach to overcome these issues and enable sequence learning with brain-like performance is neuromorphic hardware with brain-inspired learning algorithms. The Hierarchical Temporal Memory (HTM) is an algorithm inspired by the working principles of the neocortex and is able to learn and predict continuous sequences of elements. In a previous study, we showed that memristive devices, an emerging non-volatile memory technology, that is considered for energy efficient neuromorphic hardware, can be used as synapses in a biologically plausible version of the temporal memory algorithm of the HTM model. We subsequently presented a simulation study of an analog-mixed signal memristive hardware architecture that can implement the temporal learning algorithm. This architecture, which we refer to as MemSpikingTM, is based on a memristive crossbar array and a control circuitry implementing the neurons and the learning mechanism. In the study presented here, we demonstrate the functionality of the MemSpikingTM algorithm on a real memristive crossbar array, taped out in a commercially available 130nm CMOS technology node co-integrated with HfO based memristive devices. We explain the algorithm and the functionality of the crossbar array and peripheral circuitry and finally demonstrate context-dependent sequence learning using high-order sequences.","PeriodicalId":375527,"journal":{"name":"Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125171782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Additive manufacture of polymeric organometallic ferroelectric diodes (POMFeDs) for structural neuromorphic hardware 用于结构神经形态硬件的聚合物有机金属铁电二极管的增材制造

Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference Pub Date : 2023-04-11 DOI: 10.1145/3584954.3584998

Davin Browner, S. Sareh, Paul Anderson

引用次数: 0

Impact of Noisy Input on Evolved Spiking Neural Networks for Neuromorphic Systems 噪声输入对神经形态系统演化尖峰神经网络的影响

Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference Pub Date : 2023-04-11 DOI: 10.1145/3584954.3584969

Karan P. Patel, Catherine D. Schuman

引用次数: 1

SIFT-ONN: SIFT Feature Detection Algorithm Employing ONNs for Edge Detection SIFT- onn:利用onn进行边缘检测的SIFT特征检测算法

Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference Pub Date : 2023-04-11 DOI: 10.1145/3584954.3584999

Madeleine Abernot, S. Gauthier, T. Gonos, A. Todri-Sanial

{"title":"SIFT-ONN: SIFT Feature Detection Algorithm Employing ONNs for Edge Detection","authors":"Madeleine Abernot, S. Gauthier, T. Gonos, A. Todri-Sanial","doi":"10.1145/3584954.3584999","DOIUrl":"https://doi.org/10.1145/3584954.3584999","url":null,"abstract":"Mobile robot navigation tasks can be applied in various domains, such as in space, underwater, and transportation industries, among others. In navigation, robots analyze their environment from sensors and navigate safely up to target points by avoiding obstacles. Numerous methods exist to perform each navigation task. In this work, we focus on robot localization based on feature extraction algorithms using images as sensory data. ORB, and SURF are state-of-the-art algorithms for feature-based robot localization thanks to their fast computation time, even if ORB lacks precision. SIFT is state-of-the-art for high precision feature detection but it is slow and not compatible with real-time robotic applications. Thus, in our work, we explore how to speed up SIFT algorithm for real-time robot localization by employing an unconventional computing paradigm with oscillatory neural networks (ONNs). We present a hybrid SIFT-ONN algorithm that replaces the computation of Difference of Gaussian in SIFT with ONNs by performing image edge detection. We report on SIFT-ONN algorithm performances, which are similar to the state-of-the-art ORB algorithm.","PeriodicalId":375527,"journal":{"name":"Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131762133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Exploring Information-Theoretic Criteria to Accelerate the Tuning of Neuromorphic Level-Crossing ADCs 探索加速神经形态平交adc调谐的信息论准则

Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference Pub Date : 2023-04-11 DOI: 10.1145/3584954.3584994

A. Safa, Jonah Van Assche, C. Frenkel, A. Bourdoux, F. Catthoor, G. Gielen

{"title":"Exploring Information-Theoretic Criteria to Accelerate the Tuning of Neuromorphic Level-Crossing ADCs","authors":"A. Safa, Jonah Van Assche, C. Frenkel, A. Bourdoux, F. Catthoor, G. Gielen","doi":"10.1145/3584954.3584994","DOIUrl":"https://doi.org/10.1145/3584954.3584994","url":null,"abstract":"Level-crossing analog-to-digital converters (LC-ADCs) are neuromorphic, event-driven data converters that are gaining much attention for resource-constrained applications where intelligent sensing must be provided at the extreme edge, with tight energy and area budgets. LC-ADCs translate real-world analog signals (such as ECG, EEG, etc.) into sparse spiking signals, providing significant data bandwidth reduction and inducing savings of up to two orders of magnitude in area and energy consumption at the system level compared to the use of conventional ADCs. In addition, the spiking nature of LC-ADCs make their use a natural choice for ultra-low-power, event-driven spiking neural networks (SNNs). Still, the compressed nature of LC-ADC spiking signals can jeopardize the performance of downstream tasks such as signal classification accuracy, which is highly sensitive to the LC-ADC tuning parameters. In this paper, we explore the use of popular information criteria found in model selection theory for the tuning of the LC-ADC parameters. We experimentally demonstrate that information metrics such as the Bayesian, Akaike and corrected Akaike criteria can be used to tune the LC-ADC parameters in order to maximize downstream SNN classification accuracy. We conduct our experiments using both full-resolution weights and 4-bit quantized SNNs, on two different bio-signal classification tasks. We believe that our findings can accelerate the tuning of LC-ADC parameters without resorting to computationally-expensive grid searches that require many SNN training passes.","PeriodicalId":375527,"journal":{"name":"Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125026870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Shunting Inhibition as a Neural-Inspired Mechanism for Multiplication in Neuromorphic Architectures 分流抑制是神经形态结构中神经激发的增殖机制

Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference Pub Date : 2023-04-11 DOI: 10.1145/3584954.3584965

Frances S. Chance, S. Cardwell

引用次数: 1

Modeling Coordinate Transformations in the Dragonfly Nervous System 蜻蜓神经系统中的坐标变换建模

Proceedings of the 2023 Annual Neuro-Inspired Computational Elements Conference Pub Date : 2023-04-11 DOI: 10.1145/3584954.3584959

Claire Plunkett, Frances S. Chance

引用次数: 0