no code implementations • 18 Jan 2024 • Pao-Sheng Vincent Sun, Arren Glover, Chiara Bartolozzi, Arindam Basu
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.
no code implementations • 26 Dec 2023 • Biyan Zhou, Pao-Sheng Vincent Sun, Arindam Basu
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.
no code implementations • 15 Dec 2023 • Vivek Mohan, Wee Peng Tay, Arindam Basu
Furthermore, we use accuracy, sensitivity, and false detection rate to understand the effect of compression on downstream iBMI tasks, specifically, spike detection.
no code implementations • 28 Apr 2023 • Pradeep Kumar Gopalakrishnan, Chip-Hong Chang, Arindam Basu
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.
1 code implementation • 10 Apr 2023 • Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Paul Hueber, Denis Kleyko, Noah Pacik-Nelson, Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan, Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Yao-Hong Liu, Shih-Chii Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taulé, Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Alessandro Pierro, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman, Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Matthew Stewart, Kenneth Stewart, Terrence C. Stewart, Philipp Stratmann, Jonathan Timcheck, Nergis Tömen, Gianvito Urgese, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi
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.
no code implementations • 28 Feb 2023 • Chuanlin Lan, Ziyuan Yin, Arindam Basu, Rosa H. M. Chan
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.
1 code implementation • 19 Nov 2022 • Pao-Sheng Vincent Sun, Alexander Titterton, Anjlee Gopiani, Tim Santos, Arindam Basu, Wei D. Lu, Jason K. Eshraghian
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads.
no code implementations • 14 Mar 2022 • Arindam Basu, Charlotte Frenkel, Lei Deng, Xueyong Zhang
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.
no code implementations • 25 Jul 2021 • Sumon Kumar Bose, Deepak Singla, Arindam Basu
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.
no code implementations • 23 Jun 2021 • Shih-Chii Liu, John Paul Strachan, Arindam Basu
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.
no code implementations • 2 Nov 2020 • Xueyong Zhang, Jyotibdha Acharya, Arindam Basu
This paper presents a low-area and low-power consumption CMOS differential current controlled oscillator (CCO) for neuromorphic applications.
no code implementations • 21 Aug 2020 • Bapi Kar, Pradeep Kumar Gopalakrishnan, Sumon Kumar Bose, Mohendra Roy, Arindam Basu
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.
1 code implementation • 21 Jul 2020 • Andres Ussa, Chockalingam Senthil Rajen, Deepak Singla, Jyotibdha Acharya, Gideon Fu Chuanrong, Arindam Basu, Bharath Ramesh
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.
no code implementations • 31 May 2020 • Vivek Mohan, Deepak Singla, Tarun Pulluri, Andres Ussa, Pradeep Kumar Gopalakrishnan, Pao-Sheng Sun, Bharath Ramesh, Arindam Basu
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.
1 code implementation • 16 Apr 2020 • Jyotibdha Acharya, Arindam Basu
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.
no code implementations • 19 Mar 2020 • Deepak Singla, Soham Chatterjee, Lavanya Ramapantulu, Andres Ussa, Bharath Ramesh, Arindam Basu
Applications in the Internet of Video Things (IoVT) domain have very tight constraints with respect to power and area.
no code implementations • 27 Feb 2020 • Sumon Kumar Bose, Jyotibdha Acharya, Arindam Basu
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.
no code implementations • 4 Dec 2019 • Sumon Kumar Bose, Bapi Kar, Mohendra Roy, Pradeep Kumar Gopalakrishnan, Zhang Lei, Aakash Patil, Arindam Basu
However, on the detection of anomalies, the complexity of the network and computing precision are adaptively increased for accurate predictions.
no code implementations • 22 Oct 2019 • Andres Ussa, Luca Della Vedova, Vandana Reddy Padala, Deepak Singla, Jyotibdha Acharya, Charles Zhang Lei, Garrick Orchard, Arindam Basu, Bharath Ramesh
With the success of deep learning, object recognition systems that can be deployed for real-world applications are becoming commonplace.
no code implementations • 4 Oct 2019 • Jyotibdha Acharya, Andres Ussa Caycedo, Vandana Reddy Padala, Rishi Raj Sidhu Singh, Garrick Orchard, Bharath Ramesh, Arindam Basu
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).
no code implementations • 26 Feb 2019 • Jyotibdha Acharya, Vandana Padala, Arindam Basu
This paper presents a three layer spiking neural network based region proposal network operating on data generated by neuromorphic vision sensors.
no code implementations • 19 Oct 2018 • Mohendra Roy, Sumon Kumar Bose, Bapi Kar, Pradeep Kumar Gopalakrishnan, Arindam Basu
Most of these are handcrafted features, where these are manually obtained based on the nature of the raw data.
no code implementations • 25 Feb 2018 • Anand Kumar Mukhopadhyay, Indrajit Chakrabarti, Arindam Basu, Mrigank Sharad
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.
no code implementations • 3 May 2016 • Enyi Yao, Arindam Basu
In this paper, we describe a compact low-power, high performance hardware implementation of the extreme learning machine (ELM) for machine learning applications.
no code implementations • 19 Apr 2016 • Subhrajit Roy, Arindam Basu
The proposed learning rule is inspired from structural plasticity and trains the liquid through formation and elimination of synaptic connections.
no code implementations • 24 Dec 2015 • Aakash Patil, Shanlan Shen, Enyi Yao, Arindam Basu
We demonstrate a low-power and compact hardware implementation of Random Feature Extractor (RFE) core.
no code implementations • 4 Dec 2015 • Subhrajit Roy, Arindam Basu
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.
no code implementations • 3 Dec 2015 • Roshan Gopalakrishnan, Arindam Basu
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).
no code implementations • 22 Sep 2015 • Yi Chen, Enyi Yao, Arindam Basu
The chip is verified with neural data recorded in monkey finger movements experiment, achieving a decoding accuracy of 99. 3% for movement type.
no code implementations • 17 Jun 2015 • Subhrajit Roy, Phyo Phyo San, Shaista Hussain, Lee Wang Wei, Arindam Basu
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.
no code implementations • 20 Nov 2014 • Subhrajit Roy, Amitava Banerjee, Arindam Basu
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.
no code implementations • 20 Nov 2014 • Shaista Hussain, Shih-Chii Liu, Arindam Basu
This work also presents a branch-specific spike-based version of this structural plasticity rule.
no code implementations • 6 Nov 2013 • Shaista Hussain, Arindam Basu, R. Wang, Tara Julia Hamilton
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.