15 citations · 17 across the 4 of their papers we have counts for
4 papers
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…
Learnable Sparsification of Die-to-Die Communication via Spike-Based Encoding
Joshua Nardone, Ruijie Zhu, Joseph Callenes +3
Efficient communication is central to both biological and artificial intelligence (AI) systems. In biological brains, the challenge of long-range communication across regions is ad…
A Predictive Approach To Enhance Time-Series Forecasting
Skye Gunasekaran, Assel Kembay, Hugo Ladret +4
Accurate time-series forecasting is crucial in various scientific and industrial domains, yet deep learning models often struggle to capture long-term dependencies and adapt to dat…