相关论文: FlexTOE: Flexible TCP Offload with Fine-Grained Pa…

Whilst numerous areas of computing have adopted the RISC-V Instruction Set Architecture (ISA) wholesale in recent years, it is yet to become widespread in HPC. RISC-V accelerators offer a compelling option where the HPC community can…

Robust fine-tuning aims to achieve competitive in-distribution (ID) performance while maintaining the out-of-distribution (OOD) robustness of a pre-trained model when transferring it to a downstream task. Recently, projected gradient…

The Network-on-Chip (NoC) paradigm has been proposed as a favorable solution to handle the strict communication requirements between the increasingly large number of cores on a single chip. However, NoC systems are exposed to the aggressive…

Fully homomorphic encryption allows the evaluation of arbitrary functions on encrypted data. It can be leveraged to secure outsourced and multiparty computation. TFHE is a fast torus-based fully homomorphic encryption scheme that allows…

Mobile Edge Computing (MEC) enables rich services in close proximity to the end users to provide high quality of experience (QoE) and contributes to energy conservation compared with local computing, but results in increased communication…

Handling communication overhead in large-scale tensor-parallel training remains a critical challenge due to the dense, near-zero distributions of intermediate tensors, which exacerbate errors under frequent communication and introduce…

For Open IoT, we have proposed Tacit Computing technology to discover the devices that have data users need on demand and use them dynamically and an automatic GPU offloading technology as an elementary technology of Tacit Computing.…

The Diffusion Transformer (DiT) architecture is the state-of-the-art paradigm for high-fidelity image generation, underpinning models like Stable Diffusion-3 and FLUX.1. However, deploying these models on resource-constrained mobile devices…

The rapid development of deep-learning enabled task-oriented communications (TOC) significantly shifts the paradigm of wireless communications. However, the high computation demands, particularly in resource-constrained systems e.g., mobile…

As IoT ecosystems continue to expand across critical sectors, they have become prominent targets for increasingly sophisticated and large-scale malware attacks. The evolving threat landscape, combined with the sensitive nature of…

Existing cellular networks suffer from inflexible and expensive equipment, and complex control-plane protocols. To address these challenges, we present SoftCell, a scalable architecture for supporting fine-grained policies for mobile…

Deploying large language models (LLMs) in mobile and edge computing environments is constrained by limited on-device resources, scarce wireless bandwidth, and frequent model evolution. Although edge-cloud collaborative inference with…

The deployment of deep neural networks (DNNs) on resource-constrained edge devices is frequently hindered by their significant computational and memory requirements. While partitioning and distributing a DNN across multiple devices is a…

GPU-based fast Fourier transform (FFT) is extremely important for scientific computing and signal processing. However, we find the inefficiency of existing FFT libraries and the absence of fault tolerance against soft error. To address…

In this paper, we present two symbiotic optimizations to optimize recursive task parallel (RTP) programs by reducing the task creation and termination overheads. Our first optimization Aggressive Finish-Elimination (AFE) helps reduce the…

In recent years, we have witnessed unprecedented growth in using hardware-assisted Trusted Execution Environments (TEE) or enclaves to protect sensitive code and data on commodity devices thanks to new hardware security features, such as…

In this work, we present X-HEEP, an open-source, configurable, and extendible RISC-V platform for ultra-low-power edge applications (TinyAI). X-HEEP features the eXtendible Accelerator InterFace (XAIF), which enables seamless integration of…

With mobile networks expected to support services with stringent requirements that ensure high-quality user experience, the ability to apply Feed-Forward Neural Network (FFNN) models to User Equipment (UE) use cases has become critical.…

Federated learning (FL) has been recognized as a viable distributed learning paradigm which trains a machine learning model collaboratively with massive mobile devices in the wireless edge while protecting user privacy. Although various…

The proliferation of bandwidth-hungry applications and services forces datacenter (DC) administrators to optimize the utilization of available resources. Precisely, the network share of management traffic has grown significantly because DC…