5 papers
Learning to Localize Leakage of Cryptographic Sensitive Variables
Jimmy Gammell, Anand Raghunathan, Abolfazl Hashemi +1
While cryptographic algorithms such as the ubiquitous Advanced Encryption Standard (AES) are secure, *physical implementations* of these algorithms in hardware inevitably 'leak' se…
KV-CAR: KV Cache Compression using Autoencoders and KV Reuse in Large Language Models
Sourjya Roy, Shrihari Sridharan, Surya Selvam +1
As Large Language Models (LLMs) scale in size and context length, the memory requirements of the key value (KV) cache have emerged as a major bottleneck during autoregressive decod…
GradientSpace: Unsupervised Data Clustering for Improved Instruction Tuning
Shrihari Sridharan, Deepak Ravikumar, Anand Raghunathan +1
Instruction tuning is one of the key steps required for adapting large language models (LLMs) to a broad spectrum of downstream applications. However, this procedure is difficult b…
Experts are all you need: A Composable Framework for Large Language Model Inference
Shrihari Sridharan, Sourjya Roy, Anand Raghunathan +1
Large Language Models (LLMs) have achieved state-of-the-art accuracies in a variety of natural language processing (NLP) tasks. However, this success comes at the cost of increased…
Power side-channel leakage localization through adversarial training of deep neural networks
Jimmy Gammell, Anand Raghunathan, Kaushik Roy
Supervised deep learning has emerged as an effective tool for carrying out power side-channel attacks on cryptographic implementations. While increasingly-powerful deep learning-ba…