arXiv Machine Learning

Terastal: Layer-Variant-based Scheduling for Real-Time Multi-DNN Workloads on Heterogeneous Accelerators

arXiv:2606. 06818v1 Announce Type: cross Abstract: Heterogeneous DNN accelerators improve soft real-time multi-DNN execution by mapping each layer to its preferred accelerator to reduce latency.

arXiv AI

Jun 29

SEADA: An efficient methodology for optimizing mixed-precision DNNs on multi-precision spatial architectures

arXiv:2606. 27884v1 Announce Type: cross Abstract: Mixed-precision computation has been introduced in deep neural networks (DNNs) as an effective approach to reduce latency, energy consumption, and memory footprint.

By Leandro Fiorin, Marco Ronzani, Cristina Silvano

efficiency

arXiv AI

Jul 23

BRIM: Workload-Balanced Dual-Sided Bit-Serial Sparse Inference Accelerator

arXiv:2607. 19431v1 Announce Type: cross Abstract: Bit-serial accelerators exploit bit-level sparsity to reduce DNN inference cost, but existing designs exploit sparsity on only one operand, bounding the speedup.

By Varun Manjunath, Ruokai Yin, Donghyun Lee, Arkapravo Ghosh, Priyadarshini Panda

llms efficiency

arXiv AI

Aug 5

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

arXiv:2311. 17815v3 Announce Type: replace-cross Abstract: Given their increasing size and complexity, the need for efficient execution of deep neural networks has become increasingly pressing in the design of heterogeneous High-Performance Computing (HPC) and edge platforms, leading to a wide variety of proposals for specialized deep learning architectures and hardware accelerators.

By Serena Curzel, Fabrizio Ferrandi, Leandro Fiorin, Daniele Ielmini, Cristina Silvano, Francesco Conti, Luca Bompani, Luca Benini, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli, Maurizio Palesi, Giuseppe Ascia, Enrico Russo, Valeria Cardellini, Salvatore Filippone, Francesco Lo Presti, Stefania Perri

arXiv Machine Learning

Jun 30

Harvesting AI Computation at the Edge via Generic Approximation

arXiv:2606. 29518v1 Announce Type: cross Abstract: With the widespread adoption of AI in various IoT scenarios such as smart sensing and processing, AI chips have become a common component at the edge.

By Yihan Wang, Huiru Yan, Luxin Zhang, Long Cheng, Weiwei Chen, Ying Wang, Lei Zhang, Cheng Liu, Huawei Li

arXiv AI

Jul 29

SpecPrefetch: Parameter-Efficient Expert Prefetching for Sparse MoE Foundation Models

arXiv:2607. 24787v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models expand foundation model capacity through conditional expert activation, but their full expert pools remain difficult to deploy under limited accelerator memory.

By Jinwei Kong, Runqi Meng, Fanyi Wang, Wentao Qiu, Haotian Hu, Yongjian Zhou, Zhenhua Ge

efficiency benchmarks

arXiv Machine Learning

Aug 12

CARB: A Characterization-Guided Framework for CNN Inference Cost Prediction and Deployment Screening

arXiv:2608. 10506v1 Announce Type: cross Abstract: Accurate pre-deployment estimation of CNN inference cost--energy, latency, and peak memory--is increasingly critical as models are deployed on resource-constrained GPU platforms.

By Linh Nguyen, Zhixin Pan

arXiv AI

Jun 24

Bitwise Systolic Array Architecture for Runtime-Reconfigurable Multi-precision Quantized Multiplication on Hardware Accelerators

arXiv:2602. 23334v2 Announce Type: replace-cross Abstract: Neural network accelerators have been widely applied to edge devices for complex tasks like object tracking, image recognition, etc.

By Yuhao Liu, Salim Ullah, Akash Kumar

computer-vision efficiency

arXiv Machine Learning

Jul 9

Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization

arXiv:2607. 06922v1 Announce Type: new Abstract: Deep learning applications have been widely adopted on edge devices, to mitigate the privacy and latency issues of accessing cloud servers.

By Shuo Huai, Di Liu, Hao Kong, Weichen Liu, Ravi Subramaniam, Christian Makaya, Qian Lin

efficiency benchmarks safety

arXiv Machine Learning

Jul 20

Learning Faster without Deeper Networks: A*-Inspired Batch Selection for Efficient CNN Training

arXiv:2607. 15745v1 Announce Type: new Abstract: Common practice when training Convolutional Neural Networks (CNNs) is to use randomly shuffled mini-batches.

By Anxhelo Shehu, Enes Stastoli, Arben Cela

benchmarks

arXiv Machine Learning

Jul 23

TempoNet: Slack-Quantized Transformer-Guided Reinforcement Scheduler for Adaptive Deadline-Centric Real-Time Dispatchs

arXiv:2602. 18109v3 Announce Type: replace Abstract: Real-time schedulers must reason about tight deadlines under strict compute budgets.

By Rong Fu, Yibo Meng, Zeyu Zhang, Ziming Guo, Jia Yee Tan, Xiaojing Du, Simon James Fong

llms rag nlp reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jul 1

FlexViT: A Flexible FPGA-based Accelerator for Edge Vision Transformers

arXiv:2606. 31938v1 Announce Type: cross Abstract: Deploying Vision Transformer (ViT) models on edge platforms remains challenging due to their high computational demands and the architectural heterogeneity of modern hybrid ViT models, which incorporate both fully connected and convolutional layers.

By Hubert Dymarkowski, Xingjian Fu, Rappy Saha, Jude Haris, Jos\'e Cano

llms computer-vision efficiency

arXiv AI

Jun 16

A Learning Method with Gap-Aware Generation for Heterogeneous DAG Scheduling

arXiv:2603. 23249v2 Announce Type: replace-cross Abstract: Efficient scheduling of directed acyclic graphs (DAGs) is a core problem in large-scale data-intensive computing systems, where query plans, data-processing workloads, and computation graphs consist of dependent tasks competing for limited heterogeneous resource pools.

By Ruisong Zhou, Haijun Zou, Li Zhou, Chumin Sun, Zaiwen Wen

reinforcement-learning

Related stories