15 citations · 16 across the 13 of their papers we have counts for
3 papers · 1 filter
Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware
Simon Richter, Ruhai Lin, Jason Yik +4
Inference with transformer-based large language models (LLMs) is often limited by the memory-bound KV cache and quadratic attention cost. State-space models (SSMs) mitigate this th…
Neuromorphic Principles for Efficient Large Language Models on Intel Loihi 2
Steven Abreu, Sumit Bam Shrestha, Rui-Jie Zhu +1
Large language models (LLMs) deliver impressive performance but require large amounts of energy. In this work, we present a MatMul-free LLM architecture adapted for Intel's neuromo…
SDTrack: A Baseline for Event-based Tracking via Spiking Neural Networks
Yimeng Shan, Zhenbang Ren, Haodi Wu +11
Event cameras provide superior temporal resolution, dynamic range, energy efficiency, and pixel bandwidth. Spiking Neural Networks (SNNs) naturally complement event data through di…