31 citations · 104 across the 20 of their papers we have counts for
20 papers
Same Architecture, Different Capacity: Optimizer-Induced Spectral Scaling Laws
Nandan Kumar Jha, Brandon Reagen
Scaling laws have made language-model performance predictable from model size, data, and compute, but they typically treat the optimizer as a fixed training detail. We show that th…
NerVE: Nonlinear Eigenspectrum Dynamics in LLM Feed-Forward Networks
Nandan Kumar Jha, Brandon Reagen
We introduce NerVE, a unified eigenspectral framework for understanding how feed-forward networks (FFNs) in large language models (LLMs) organize and regulate information flow in h…
A Spatial Array for Spectrally Agile Wireless Processing
Ali Rasteh, Andrew Hennessee, Ishaan Shivhare +3
Massive MIMO is a cornerstone of next-generation wireless communication, offering significant gains in capacity, reliability, and energy efficiency. However, to meet emerging deman…
Network and Compiler Optimizations for Efficient Linear Algebra Kernels in Private Transformer Inference
Karthik Garimella, Negar Neda, Austin Ebel +2
Large language model (LLM) based services are primarily structured as client-server interactions, with clients sending queries directly to cloud providers that host LLMs. This appr…
Spectral Scaling Laws in Language Models: How Effectively Do Feed-Forward Networks Use Their Latent Space?
Nandan Kumar Jha, Brandon Reagen
As large language models (LLMs) scale, the question is not only how large they become, but how much of their capacity is effectively utilized. Existing scaling laws relate model si…
A Random Matrix Theory Perspective on the Learning Dynamics of Multi-head Latent Attention
Nandan Kumar Jha, Brandon Reagen
In this work, we study how multi-head latent attention (MLA), a popular strategy for compressing key/value memory, affects a transformer's internal capacity during pretraining. Usi…
Entropy-Guided Attention for Private LLMs
Nandan Kumar Jha, Brandon Reagen
The pervasiveness of proprietary language models has raised critical privacy concerns, necessitating advancements in private inference (PI), where computations are performed direct…
TruncFormer: Private LLM Inference Using Only Truncations
Patrick Yubeaton, Jianqiao Cambridge Mo, Karthik Garimella +4
Private inference (PI) serves an important role in guaranteeing the privacy of user data when interfacing with proprietary machine learning models such as LLMs. However, PI remains…
ReLU's Revival: On the Entropic Overload in Normalization-Free Large Language Models
Nandan Kumar Jha, Brandon Reagen
LayerNorm is a critical component in modern large language models (LLMs) for stabilizing training and ensuring smooth optimization. However, it introduces significant challenges in…
AERO: Entropy-Guided Framework for Private LLM Inference
Nandan Kumar Jha, Brandon Reagen
Privacy-preserving computation enables language model inference directly on encrypted data yet suffers from prohibitive latency and communication overheads, primarily due to nonlin…
PriViT: Vision Transformers for Fast Private Inference
Naren Dhyani, Jianqiao Mo, Minsu Cho +4
The Vision Transformer (ViT) architecture has emerged as the backbone of choice for state-of-the-art deep models for computer vision applications. However, ViTs are ill-suited for…
DeepReShape: Redesigning Neural Networks for Efficient Private Inference
Nandan Kumar Jha, Brandon Reagen
Prior work on Private Inference (PI) -- inferences performed directly on encrypted input -- has focused on minimizing a network's ReLUs, which have been assumed to dominate PI late…
Characterizing and Optimizing End-to-End Systems for Private Inference
Karthik Garimella, Zahra Ghodsi, Nandan Kumar Jha +2
In two-party machine learning prediction services, the client's goal is to query a remote server's trained machine learning model to perform neural network inference in some applic…
Selective Network Linearization for Efficient Private Inference
Minsu Cho, Ameya Joshi, Siddharth Garg +2
Private inference (PI) enables inference directly on cryptographically secure data.While promising to address many privacy issues, it has seen limited use due to extreme runtimes.…
CryptoNite: Revealing the Pitfalls of End-to-End Private Inference at Scale
Karthik Garimella, Nandan Kumar Jha, Zahra Ghodsi +2
The privacy concerns of providing deep learning inference as a service have underscored the need for private inference (PI) protocols that protect users' data and the service provi…
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning
Karthik Garimella, Nandan Kumar Jha, Brandon Reagen
Privacy concerns in client-server machine learning have given rise to private inference (PI), where neural inference occurs directly on encrypted inputs. PI protects clients' perso…
Sphynx: ReLU-Efficient Network Design for Private Inference
Minsu Cho, Zahra Ghodsi, Brandon Reagen +2
The emergence of deep learning has been accompanied by privacy concerns surrounding users' data and service providers' models. We focus on private inference (PI), where the goal is…
Circa: Stochastic ReLUs for Private Deep Learning
Zahra Ghodsi, Nandan Kumar Jha, Brandon Reagen +1
The simultaneous rise of machine learning as a service and concerns over user privacy have increasingly motivated the need for private inference (PI). While recent work demonstrate…
DeepReDuce: ReLU Reduction for Fast Private Inference
Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg +1
The recent rise of privacy concerns has led researchers to devise methods for private neural inference -- where inferences are made directly on encrypted data, never seeing inputs.…
CryptoNAS: Private Inference on a ReLU Budget
Zahra Ghodsi, Akshaj Veldanda, Brandon Reagen +1
Machine learning as a service has given raise to privacy concerns surrounding clients' data and providers' models and has catalyzed research in private inference (PI): methods to p…