11 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…
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…
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…