Heterogeneous Computing

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6 papers in the last four weeks, against 1 the four weeks before. 0.1% of all new papers.

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Rethinking Streaming-Perception Evaluation on Heterogeneous Edge Platforms
Oct 4, 2026Misun Yu, Jinyoung Moon, Jemin LeeEdge Inference Heterogeneous Computing
Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea · Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Republic of Korea
HAPMoE: Heterogeneity-Aware Automatic Parallelism Planning for Mixture-of-Experts Models Training
Sep 30, 2026Mengyuan Fan, Peizhuang Cong, Zixiao Huang +7Expert Parallelism Heterogeneous Computing
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · Infinigence AI · Tsinghua University
Kafila: Serving Large Language Models on a Trusted Set of Heterogeneous Commodity Machines
Sep 28, 2026Murtaza Rangwala, Richard O. Sinnott, Rajkumar BuyyaLLM Inference LLM Inference Scheduling
Quantum Cloud Computing and Distributed Systems (qCLOUDS) Lab, School of Computing and Information Systems, The University of Melbourne, Australia
Rethinking Heterogeneous System Disaggregation for Subquadratic Attention
Sep 14, 2026Arya Tschand, Yaosheng Fu, Vikram Sharma Mailthody +6Disaggregated LLM Serving LLM Inference Acceleration
Harvard University Cambridge, Massachusetts, USA · NVIDIA Santa Clara, California, USA
HiGFRL: Hierarchical Graph Fusion-Driven Reinforcement Learning for Dependency-Aware Task Scheduling in Heterogeneous Cloud
Sep 14, 2026Tiangang Li, Shi Ying, Xiangbo TianReinforcement Learning Heterogeneous Computing
School of Computer Science, Wuhan University, Wuhan 430072, China
Fidelity-Aware Scheduling of Quantum Circuits on Multi-QPU Systems
Sep 9, 2026Innocenzo Fulginiti, Antonio Tudisco, Salvatore Zammuto +7High-Performance Computing Heterogeneous Computing
Technical University of Munich, Munich, Germany · Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy · MQSC, Garching near Munich, Germany +1
Para-Pipe: Exploiting Hierarchical Operator Parallelism of ML Computational Graphs on SoCs
Sep 3, 2026Yujie Zhang, Huiying Lan, Ehsan Aghapour +5Efficient Neural Network Inference Energy-Efficient ML
School of Computing, National University of Singapore, Singapore · Parallel Computer Systems, the University of Amsterdam, 1098 XH Amsterdam, The Netherlands · Information Technology Department, Black Sesame Technologies, San Jose, CA 95131, USA
Architectural Implications of Agentic AI Workflows
Aug 5, 2026Jirong Yang, Peizhe Liu, Chaojie Zhang +1LLM Inference Scheduling Agentic Workflows
The University of Texas at Austin · Microsoft Azure
Gleam: Adaptive Network-Efficient CUDA API Remoting for Cross-Device GPU Sharing over LANs
Jul 25, 2026Zhihao Xu, Hao Zhong, Zeting Zhou +9Heterogeneous Computing Distributed Inference
Shanghai Jiao Tong University · Lenovo Information Products (Shenzhen) Ltd · Lenovo (Beijing) Ltd
FusionML: Prefill, Not Decode - Mechanism and Boundaries of CPU+GPU Co-Execution on Unified-Memory Apple Silicon
Jul 24, 2026Om MohiteLLM Serving LLM Inference Acceleration
Independent Researcher, Mumbai, India
GLidE-SLAM: GL-Accelerated Indirect-Direct Embedded SLAM
Jul 18, 2026Carlos A. Pinheiro de Sousa, Heiko Hamann, Oliver DeussenSimultaneous Localization and Mapping Visual Odometry
The authors are with the Department of Computer and Information Science, University of Konstanz, 78464 Konstanz, Germany.
EdgeFaaS: A Function-based Framework for Edge Computing
Jul 16, 2026Neha Vadnere, Yu-Ting Wang, Yitao Chen +2Edge Computing Internet of Things
Arizona State University, Tempe, USA
Edge Physical AI Deployment of Vision Transformers on Heterogeneous Edge GPU Targeting Autonomous Vehicles
Jul 12, 2026Ashiyana Abdul Majeed, Mahmoud Meribout, Neethu Joseph +2Efficient Transformer Inference Edge Inference
Department of Computer and Information Engineering, Khalifa University, Abu Dhabi, UAE · Department of Electrical Engineering and Computer Science at the University of California, Irvine, CA 92697, USA
Optimizing ML Workload Partitioning between CPUs and CIM Accelerators for Heterogeneous Computing
Jul 6, 2026Joel Klein, Rebecca Pelke, Roberto Laudani +2Compute-in-Memory Heterogeneous Computing
Institute for Communication Technologies and Embedded Systems, RWTH Aachen University, Germany
Fine-Grained Computation Offload for Off-the-Shelf Servers in Tens of Lines
Jul 2, 2026Bojie LiHeterogeneous Computing
Pine AI
SMEPilot: Characterizing and Optimizing LLM Inference with Scalable Matrix Extensions
Jun 15, 2026Feiyang Chen, Haibo ChenLLM Inference Efficiency LLM Inference Acceleration
IPADS, Shanghai Jiao Tong University
Achieving Cloud-Grade SLOs for Local Mixture-of-Experts Inference through CPU-GPU Hybrid Design
Jun 9, 2026Wenxin Wang, Yule Hou, Yu Ji +2Expert Parallelism On-Device Language Model Inference
1Tsinghua University · 2Xingyun Integrated Circuits Co., Ltd. · 3Beijing National Research Center for Information Science and Technology
Terastal: Layer-Variant-based Scheduling for Real-Time Multi-DNN Workloads on Heterogeneous Accelerators
Jun 5, 2026Sing-Yao Wu, Fengshuo Song, Eli BozorgzadehAI Accelerator Inference Real-Time Scheduling
Department of Computer Science, University of California, Irvine, CA, USA
Brief Announcement: Generative Markov Model for Distributed Computing Systems
Jun 2, 2026Alfreds Lapkovskis, Ali Beikmohammadi, Sindri Magnússon +1Heterogeneous Computing Generative Modeling
Department of Computer and Systems Sciences, Stockholm University, Sweden
CodegenBench: Can LLMs Write Efficient Code Across Architectures?
Jun 1, 2026Jie Li, Wenzhao Wu, Junqi Hu +5High-Performance Computing Code Generation
1Sun Yat-sen University · 2National Supercomputing Center in Wuxi · 3National Supercomputing Center in Shenzhen +2
HetCCL: Enabling Collective Communication For Mixed-Vendor Heterogeneous Clusters
May 29, 2026Yuejie Wang, Tao Chang, Yuanyuan Zhao +10Communication-Efficient Distributed Training High-Performance Computing
Peking University · Beijing Academy of Artificial Intelligence · Infrawaves +1
UniLab: A Heterogeneous Architecture for Robot RL Beyond GPU-Dominant Paradigms
May 28, 2026Yufei Jia, Zhanxiang Cao, Mingrui Yu +48Robot Policy Learning Robotics Simulation
A Methodology to Assess Power Modeling in Energy-Aware Federated Learning on Heterogeneous Mobile Devices
May 26, 2026Chaimae Jallouli, Karim Boubouh, Robert BasmadjianEnergy-Efficient ML Heterogeneous Computing
Mohammed VI Polytechnic University, Benguerir, Morocco · Khalifa University, Abu Dhabi, UAE
GEM: GPU-Variability-Aware Expert to GPU Mapping for MoE Systems
May 19, 2026Sourish Wawdhane, Avinash Kumar, Poulami DasExpert Load Balancing Mixture-of-Experts Inference
The University of Texas at Austin
Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach
May 18, 2026Yaorong Huang, Jingtao Luo, Xuechao WangFederated RL Edge Computing
∗The Hong Kong University of Science and Technology (Guangzhou) · †Chengdu Neusoft University
CoX-MoE: Coalesced Expert Execution for High-Throughput MoE Inference with AMX-Enabled CPU-GPU Co-Execution
May 18, 2026Muyoung Son, Yi Chen, Seungjae Yoo +2Expert Offloading GPU Acceleration
KAIST · Daejeon, Republic of Korea
Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
May 13, 2026Ammar Mahran, Artavazd Maranjyan, Peter RichtárikDistributed Optimization Nonconvex Stochastic Optimization
KAUST
Heterogeneous SoC Integrating an Open-Source Recurrent SNN Accelerator for Neuromorphic Edge Computing on FPGA
May 12, 2026Michelangelo Barocci, Vittorio Fra, Enrico Macii +1Edge Computing Heterogeneous Computing
Department of Computer and Control Engineering, Politecnico di Torino, Turin, Italy · Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, Turin, Italy
An Efficient Hybrid Sparse Attention with CPU-GPU Parallelism for Long-Context Inference
May 8, 2026Feiyu Yao, Zhixiong Niu, Xiaqing Li +3GPU Acceleration Self-Attention
Beijing University of Technology Beijing, China · Microsoft Research Beijing, China · Beijing Jiaotong University Beijing, China
HCInfer: An Efficient Inference System via Error Compensation for Resource-Constrained Devices
May 7, 2026Shen Xu, Xiangwen Zhuge, Zhe Xu +3LLM Compression Memory-Efficient Inference
Tsinghua University · Huazhong University of Science and Technology

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Rethinking Streaming-Perception Evaluation on Heterogeneous Edge Platforms

Misun Yu, Jinyoung Moon, Jemin Lee

Electronics and Telecommunications Research Institute (ETRI), Daejeon, Republic of Korea · Division of Electronics and Information Engineering, Jeonbuk National University, Jeonju, Republic of Korea

Edge Inference Heterogeneous Computing

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Multi-camera streaming perception is increasingly deployed on heterogeneous edge platforms shared with co-resident workloads, yet accelerator placement is often evaluated using isolated single-stream experiments and mean streaming average precision (sAP). Using two end-to-end pipelines on a single GPU--NPU platform, we show that isolated evaluation can mis-rank deployment-time placement. Although the GPU pipeline is preferred in isolation, GPU-localized contention introduces deadline misses that make detections stale and can reverse the preferred placement before full GPU saturation. The NPU pipeline is less accurate than the GPU pipeline on small and medium objects in isolation, but nearly matches it on large objects. The largest absolute sAP losses in our latency and contention experiments occur for large objects. In our four-stream experiments, the preferred placement depends on which path becomes stale, and increasing GPU-side contention shifts the best placement from All-GPU to All-NPU. Under a GPU-saturating vision--language co-tenant, All-NPU achieves

5.2\times

the worst-stream sAP of All-GPU. Because mean sAP can hide severe single-stream degradation, evaluation should report contention sweeps, deadline-miss rates on both paths, and worst-stream sAP alongside mean sAP.

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Latest papers 42