64 citations · 147 across the 44 of their papers we have counts for
14 papers · 1 filter
Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology
Souvik Kundu, Rui-Jie Zhu, Akhilesh Jaiswal +1
Neuromorphic computing and, in particular, spiking neural networks (SNNs) have become an attractive alternative to deep neural networks for a broad range of signal processing appli…
GenQ: Quantization in Low Data Regimes with Generative Synthetic Data
Yuhang Li, Youngeun Kim, Donghyun Lee +2
In the realm of deep neural network deployment, low-bit quantization presents a promising avenue for enhancing computational efficiency. However, it often hinges on the availabilit…
Junk DNA Hypothesis: Pruning Small Pre-Trained Weights Irreversibly and Monotonically Impairs "Difficult" Downstream Tasks in LLMs
Lu Yin, Ajay Jaiswal, Shiwei Liu +2
We present Junk DNA Hypothesis by adopting a novel task-centric angle for the pre-trained weights of large language models (LLMs). It has been believed that weights in LLMs contain…
Fusing Models with Complementary Expertise
Hongyi Wang, Felipe Maia Polo, Yuekai Sun +3
Training AI models that generalize across tasks and domains has long been among the open problems driving AI research. The emergence of Foundation Models made it easier to obtain e…
Sensi-BERT: Towards Sensitivity Driven Fine-Tuning for Parameter-Efficient BERT
Souvik Kundu, Sharath Nittur Sridhar, Maciej Szankin +1
Large pre-trained language models have recently gained significant traction due to their improved performance on various down-stream tasks like text classification and question ans…
InstaTune: Instantaneous Neural Architecture Search During Fine-Tuning
Sharath Nittur Sridhar, Souvik Kundu, Sairam Sundaresan +2
One-Shot Neural Architecture Search (NAS) algorithms often rely on training a hardware agnostic super-network for a domain specific task. Optimal sub-networks are then extracted fr…
FireFly A Synthetic Dataset for Ember Detection in Wildfire
Yue Hu, Xinan Ye, Yifei Liu +7
This paper presents "FireFly", a synthetic dataset for ember detection created using Unreal Engine 4 (UE4), designed to overcome the current lack of ember-specific training resourc…
NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations
Yonggan Fu, Ye Yuan, Souvik Kundu +3
Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and…
Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference
Souvik Kundu, Yuke Zhang, Dake Chen +1
Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference. In this paper, we present a model optimiza…
C2PI: An Efficient Crypto-Clear Two-Party Neural Network Private Inference
Yuke Zhang, Dake Chen, Souvik Kundu +3
Recently, private inference (PI) has addressed the rising concern over data and model privacy in machine learning inference as a service. However, existing PI frameworks suffer fro…
Technology-Circuit-Algorithm Tri-Design for Processing-in-Pixel-in-Memory (P2M)
Md Abdullah-Al Kaiser, Gourav Datta, Sreetama Sarkar +6
The massive amounts of data generated by camera sensors motivate data processing inside pixel arrays, i.e., at the extreme-edge. Several critical developments have fueled recent in…
ViTA: A Vision Transformer Inference Accelerator for Edge Applications
Shashank Nag, Gourav Datta, Souvik Kundu +2
Vision Transformer models, such as ViT, Swin Transformer, and Transformer-in-Transformer, have recently gained significant traction in computer vision tasks due to their ability to…
Sparse Mixture Once-for-all Adversarial Training for Efficient In-Situ Trade-Off Between Accuracy and Robustness of DNNs
Souvik Kundu, Sairam Sundaresan, Sharath Nittur Sridhar +3
Existing deep neural networks (DNNs) that achieve state-of-the-art (SOTA) performance on both clean and adversarially-perturbed images rely on either activation or weight condition…
Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference
Souvik Kundu, Shunlin Lu, Yuke Zhang +2
The large number of ReLU non-linearity operations in existing deep neural networks makes them ill-suited for latency-efficient private inference (PI). Existing techniques to reduce…