5 accepted papers
Pruning is a practical approach to compress large language models (LLMs), but it can amplify text degeneration, especially repetition loops, even when perplexity and task accuracy remain largely unchanged. In this work, we present a token-level analysis of this failure mode by viewing decoding as a …
Quantization techniques such as BitsAndBytes, AWQ, and GPTQ are widely used as a standard method in deploying large language models but often degrades accuracy when using low-bit representations, e.g., 4 bits. Low-rank correction methods (e.g., LQER, QERA, ASER) has been proposed to mitigate this is…
Traditional federated learning frameworks, often reliant on deep neural networks, face challenges related to computational demands and privacy risks. In this paper, we present a novel Hyperdimensional (HD) Computing-based federated learning framework designed for resource-constrained mobile robots.
Efficiency and performance are significant challenges in applying Machine Learning (ML) to robotics, especially in energy-constrained real-world scenarios. In this context, Hyperdimensional Computing offers an energy-efficient alternative but has been underexplored in robotics. We introduce ReactHD,…
In this paper, we propose an efficient framework to accelerate a lightweight brain-inspired learning solution, hyperdimensional computing (HDC), on existing edge systems. Through algorithm-hardware co-design, we optimize the HDC models to run them on the low-power host CPU and machine learning accel…