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DARE: Diffusion Language Model Activation Reuse for Efficient Inference
Natalia Frumkin, Bokun Wang, Hung-Yueh Chiang +3
Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to auto-regressive (AR) models, offering greater expressive capacity and potential for parallel gene…
UniQL: Unified Quantization and Low-rank Compression for Adaptive Edge LLMs
Hung-Yueh Chiang, Chi-Chih Chang, Yu-Chen Lu +4
Deploying large language models (LLMs) on mobile platforms faces significant challenges due to the limited memory and shared computational resources of the device. Resource availab…
Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models
Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin +3
State Space Models (SSMs) are emerging as a compelling alternative to Transformers because of their consistent memory usage and high performance. Despite this, scaling up SSMs on c…
Quamba: A Post-Training Quantization Recipe for Selective State Space Models
Hung-Yueh Chiang, Chi-Chih Chang, Natalia Frumkin +2
State Space Models (SSMs) have emerged as an appealing alternative to Transformers for large language models, achieving state-of-the-art accuracy with constant memory complexity wh…
SCAN-Edge: Finding MobileNet-speed Hybrid Networks for Diverse Edge Devices via Hardware-Aware Evolutionary Search
Hung-Yueh Chiang, Diana Marculescu
Designing low-latency and high-efficiency hybrid networks for a variety of low-cost commodity edge devices is both costly and tedious, leading to the adoption of hardware-aware neu…
FOX-NAS: Fast, On-device and Explainable Neural Architecture Search
Chia-Hsiang Liu, Yu-Shin Han, Yuan-Yao Sung +3
Neural architecture search can discover neural networks with good performance, and One-Shot approaches are prevalent. One-Shot approaches typically require a supernet with weight s…