janardhan rao

Mixture of Experts (MoE) architectures have emerged as a dominant paradigm for scaling Large Language Models (LLMs). However, MoE inference on conventional hardware is constrained by three fundamental bottlenecks. These encompass the massive memory bandwidth required to fetch non-contiguous expert weights, the non-deterministic scatter-gather traffic generated by input-dependent token routing, and the tail-latency dependency imposed by synchronous expert output aggregation.

Processing-In-Memory (PIM) has emerged as a promising technology for accelerating machine learning (ML) workloads. Specifically, non-volatile memory-based PIM architectures have enabled effective ML acceleration due to their ability to perform energy-efficient matrix-vector multiplication operations. However, these devices suffer from non-idealities such as thermal noise.

[Submitted on 14 Jul 2026] Title:SteinGate: Tail-Sensitive Safe Reinforcement Learning via Stein Discrepancy View a PDF of the paper titled SteinGate: Tail-Sensitive Safe Reinforcement Learning via Stein Discrepancy, by Yassine Chemingui and 3 other authors View PDF HTML (experimental) Abstract:Safe reinforcement learning typically enforces safety by bounding expected cumulative costs, a criterion that often fails to detect rare but catastrophic tail events.