no code implementations • 26 Apr 2024 • Yi Jiang, Sen Lu, Abhronil Sengupta
Spiking Neural Networks (SNNs), recognized as the third generation of neural networks, are known for their bio-plausibility and energy efficiency, especially when implemented on neuromorphic hardware.
no code implementations • 1 Feb 2024 • Jiaqi Lin, Sen Lu, Malyaban Bal, Abhronil Sengupta
However, training SNNs is challenging due to the non-differentiable nature of the spiking mechanism.
no code implementations • 25 Dec 2023 • Zhuangyu Han, Abhronil Sengupta
Neuromorphic computing systems, where information is transmitted through action potentials in a bio-plausible fashion, is gaining increasing interest due to its promise of low-power event-driven computing.
no code implementations • 18 Dec 2023 • Md Zesun Ahmed Mia, Malyaban Bal, Abhronil Sengupta
Preliminary attempts at incorporating the critical role of astrocytes - cells that constitute more than 50% of human brain cells - in brain-inspired neuromorphic computing remain in infancy.
1 code implementation • 21 Aug 2023 • Malyaban Bal, Abhronil Sengupta
Moreover, the convergence of average spiking rate of neurons at equilibrium is utilized to develop a novel ANN-SNN knowledge distillation based technique wherein we use a pre-trained BERT model as "teacher" to train our "student" spiking architecture.
no code implementations • 8 Jul 2023 • Sen Lu, Abhronil Sengupta
Spike-Timing-Dependent Plasticity (STDP) is an unsupervised learning mechanism for Spiking Neural Networks (SNNs) that has received significant attention from the neuromorphic hardware community.
1 code implementation • 15 Sep 2022 • Zhuangyu Han, A N M Nafiul Islam, Abhronil Sengupta
While neuromorphic computing architectures based on Spiking Neural Networks (SNNs) are increasingly gaining interest as a pathway toward bio-plausible machine learning, attention is still focused on computational units like the neuron and synapse.
1 code implementation • 14 Sep 2022 • Malyaban Bal, Abhronil Sengupta
However, by definition, EP requires the input to the model (a convergent RNN) to be static in both the phases of training.
no code implementations • 8 Sep 2020 • Mehul Rastogi, Sen Lu, Nafiul Islam, Abhronil Sengupta
Neuromorphic computing is emerging to be a disruptive computational paradigm that attempts to emulate various facets of the underlying structure and functionalities of the brain in the algorithm and hardware design of next-generation machine learning platforms.
no code implementations • 11 Jun 2020 • Kaveri Mahapatra, Sen Lu, Abhronil Sengupta, Nilanjan Ray Chaudhuri
Accurate online classification of disturbance events in a transmission network is an important part of wide-area monitoring.
1 code implementation • 24 Feb 2020 • Sen Lu, Abhronil Sengupta
On-chip edge intelligence has necessitated the exploration of algorithmic techniques to reduce the compute requirements of current machine learning frameworks.
no code implementations • 16 Nov 2019 • Akul Malhotra, Sen Lu, Kezhou Yang, Abhronil Sengupta
Uncertainty plays a key role in real-time machine learning.
no code implementations • 13 Nov 2019 • Kezhou Yang, Akul Malhotra, Sen Lu, Abhronil Sengupta
Probabilistic machine learning enabled by the Bayesian formulation has recently gained significant attention in the domain of automated reasoning and decision-making.
Emerging Technologies
no code implementations • 31 Aug 2018 • Shubham Jain, Abhronil Sengupta, Kaushik Roy, Anand Raghunathan
We present RxNN, a fast and accurate simulation framework to evaluate large-scale DNNs on resistive crossbar systems.
1 code implementation • 7 Feb 2018 • Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, Kaushik Roy
Over the past few years, Spiking Neural Networks (SNNs) have become popular as a possible pathway to enable low-power event-driven neuromorphic hardware.
no code implementations • 26 Aug 2017 • Aayush Ankit, Abhronil Sengupta, Kaushik Roy
Implementation of Neuromorphic Systems using post Complementary Metal-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged as a promising solution to enable low-power acceleration of neural networks.
no code implementations • 20 Feb 2017 • Aayush Ankit, Abhronil Sengupta, Priyadarshini Panda, Kaushik Roy
In this paper, we propose RESPARC - a reconfigurable and energy efficient architecture built-on Memristive Crossbar Arrays (MCA) for deep Spiking Neural Networks (SNNs).
no code implementations • 29 Sep 2015 • Priyadarshini Panda, Abhronil Sengupta, Kaushik Roy
Deep learning neural networks have emerged as one of the most powerful classification tools for vision related applications.
no code implementations • 29 Sep 2015 • Priyadarshini Panda, Swagath Venkataramani, Abhronil Sengupta, Anand Raghunathan, Kaushik Roy
We propose a 2-stage hierarchical classification framework, with increasing levels of complexity, wherein the first stage is trained to recognize the broad representative semantic features relevant to the object of interest.