User profiles for Nitin Rathi
Nitin RathiApplied Research Scientist, Meta Verified email at meta.com Cited by 1485 |
Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation
Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes)
which can potentially lead to higher energy-efficiency in neuromorphic hardware …
which can potentially lead to higher energy-efficiency in neuromorphic hardware …
Diet-snn: Direct input encoding with leakage and threshold optimization in deep spiking neural networks
Bio-inspired spiking neural networks (SNNs), operating with asynchronous binary signals (or
spikes) distributed over time, can potentially lead to greater computational efficiency on …
spikes) distributed over time, can potentially lead to greater computational efficiency on …
Exploring neuromorphic computing based on spiking neural networks: Algorithms to hardware
Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …
attention lately due to its promise of reducing the computational energy, latency, as well as …
Diet-snn: A low-latency spiking neural network with direct input encoding and leakage and threshold optimization
Bioinspired spiking neural networks (SNNs), operating with asynchronous binary signals (or
spikes) distributed over time, can potentially lead to greater computational efficiency on …
spikes) distributed over time, can potentially lead to greater computational efficiency on …
Non-conventional machining of nickel based superalloys: A review
N Rathi, P Kumar, A Gupta - Materials Today: Proceedings, 2023 - Elsevier
Nickel, the 24th most abundant element, is found in 0.016 % of the earth's crust. Addition of
Nickel is used as an alloying element in hardenable steels, stainless steels, high-…
Nickel is used as an alloying element in hardenable steels, stainless steels, high-…
STDP-based pruning of connections and weight quantization in spiking neural networks for energy-efficient recognition
Spiking neural networks (SNNs) with a large number of weights and varied weight distribution
can be difficult to implement in emerging in-memory computing hardware due to the …
can be difficult to implement in emerging in-memory computing hardware due to the …
Towards ultra low latency spiking neural networks for vision and sequential tasks using temporal pruning
Spiking Neural Networks (SNNs) can be energy efficient alternatives to commonly used deep
neural networks (DNNs). However, computation over multiple timesteps increases latency …
neural networks (DNNs). However, computation over multiple timesteps increases latency …
Inherent adversarial robustness of deep spiking neural networks: Effects of discrete input encoding and non-linear activations
In the recent quest for trustworthy neural networks, we present Spiking Neural Network (SNN)
as a potential candidate for inherent robustness against adversarial attacks. In this work, …
as a potential candidate for inherent robustness against adversarial attacks. In this work, …
Enhancing Resilience: Flood Vulnerability Assessment in the Uttarakhand Himalaya
This chapter delves into the critical task of categorizing Chamoli District in Uttarakhand, India,
into five distinct flood-vulnerable zones by meticulously evaluating the influence of …
into five distinct flood-vulnerable zones by meticulously evaluating the influence of …
Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease
Background Parkinson's Disease (PD) is one of the most prevailing neurodegenerative
diseases. Improving diagnoses and treatments of this disease is essential, as currently there …
diseases. Improving diagnoses and treatments of this disease is essential, as currently there …