8 papers
Compiler-Driven Approximation Tuning for Hyperdimensional Computing
Xavier Routh, Abdul Rafae Noor, Akash Kothari +4
As Moore's law reaches its physical and economic limits, domain-specific approaches are increasingly employed to accelerate machine learning workloads. Hyperdimensional Computing (…
GenDRAM:Hardware-Software Co-Design of General Platform in DRAM
Tsung-Han Lu, Weihong Xu, Tajana Rosing
Dynamic programming (DP) algorithms, such as All-Pairs Shortest Path (APSP) and genomic sequence alignment, are fundamental to many scientific domains but are severely bottlenecked…
Divide and Learn: Multi-Objective Combinatorial Optimization at Scale
Esha Singh, Dongxia Wu, Chien-Yi Yang +3
Multi-objective combinatorial optimization seeks Pareto-optimal solutions over exponentially large discrete spaces, yet existing methods sacrifice generality, scalability, or theor…
CHIME: Chiplet-based Heterogeneous Near-Memory Acceleration for Edge Multimodal LLM Inference
Yanru Chen, Runyang Tian, Yue Pan +3
The proliferation of large language models (LLMs) is accelerating the integration of multimodal assistants into edge devices, where inference is executed under stringent latency an…
SLIM: A Heterogeneous Accelerator for Edge Inference of Sparse Large Language Model via Adaptive Thresholding
Weihong Xu, Haein Choi, Po-kai Hsu +2
Large language models (LLMs) have demonstrated exceptional proficiency in understanding and generating human language, but efficient inference on resource-constrained embedded devi…
FSL-HDnn: A 5.7 TOPS/W End-to-end Few-shot Learning Classifier Accelerator with Feature Extraction and Hyperdimensional Computing
Haichao Yang, Chang Eun Song, Weihong Xu +6
This paper introduces FSL-HDnn, an energy-efficient accelerator that implements the end-to-end pipeline of feature extraction, classification, and on-chip few-shot learning (FSL) t…
E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning
Le Zhang, Onat Gungor, Flavio Ponzina +1
Ensemble learning is a meta-learning approach that combines the predictions of multiple learners, demonstrating improved accuracy and robustness. Nevertheless, ensembling models li…
SpecHD: Hyperdimensional Computing Framework for FPGA-based Mass Spectrometry Clustering
Sumukh Pinge, Weihong Xu, Jaeyoung Kang +4
Mass spectrometry-based proteomics is a key enabler for personalized healthcare, providing a deep dive into the complex protein compositions of biological systems. This technology…