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Shahin Nazarian

4 accepted papers

2026

ERMoE: Eigen-Reparameterized Mixture-of-Experts for Stable Routing and Interpretable Specialization

CVPR 2026

Mixture-of-Experts (MoE) models expand capacity via sparse expert activation, but routing logits can misalign with expert structure (unstable routing, underutilization) and load imbalance can create stragglers. Auxiliary load-balancing losses reduce disparity but often weaken specialization and down

Cited by 0SourceScholar
2025

Exploiting Application-to-Architecture Dependencies for Designing Scalable OS

ICASSP 2025accepted

With the advent of hundreds of cores on a chip to accelerate applications, the operating system (OS) needs to exploit the existing parallelism provided by the underlying hardware resources to determine the right amount of processes to be mapped on the multi-core systems. However, the existing OS is…

Cited by 0SourceScholar
2024

A Structure-Aware Framework for Learning Device Placements on Computation Graphs

NeurIPS 2024poster

Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks. The device placement problem aims to identify optimal allocations of those nodes to a set of (potentially heterogeneous…

2024

Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks

ICASSP 2024accepted

Backpropagation (BP) has been a successful optimization technique for deep learning models. However, its limitations, such as backward- and update-locking, and its biological implausibility, hinder the concurrent updating of layers and do not mimic the local learning processes observed in the human…

Cited by 0SourceScholar