Related papers: Fault Tolerance for Remote Memory Access Programmi…

Resistive Random-Access Memory (ReRAM) crossbar arrays are promising candidates for in-situ matrix-vector multiplication (MVM), a frequent operation in Deep Learning algorithms. Despite their advantages, these emerging non-volatile memories…

Attributing APT (Advanced Persistent Threat) malware to their respective groups is crucial for threat intelligence and cybersecurity. However, APT adversaries often conceal their identities, rendering attribution inherently adversarial.…

With the widespread use of machine learning, concerns over its security and reliability have become prevalent. As such, many have developed defenses to harden neural networks against adversarial examples, imperceptibly perturbed inputs that…

Machine unlearning enables the removal of specific data from ML models to uphold the right to be forgotten. While approximate unlearning algorithms offer efficient alternatives to full retraining, this work reveals that they fail to…

The rapid evolution of Large Language Model (LLM) agents has necessitated robust memory systems to support cohesive long-term interaction and complex reasoning. Benefiting from the strong capabilities of LLMs, recent research focus has…

Memory disaggregation provides efficient memory utilization across network-connected systems. It allows a node to use part of memory in remote nodes in the same cluster. Recent studies have improved RDMA-based memory disaggregation systems,…

Ensuring uninterrupted data flow in modern networks requires robust fault-tolerant mechanisms, especially in environments where reliability and responsiveness are critical. This paper presents the design and simulation of a fault-tolerant…

On-chip memory (usually based on Static RAMs-SRAMs) are crucial components for various computing devices including heterogeneous devices, e.g., GPUs, FPGAs, ASICs to achieve high performance. Modern workloads such as Deep Neural Networks…

Reliability has taken centre stage in the development of high-performance computing processors. A Surge of interest is noticeable in recent times in formulating fault and failure models, understanding failure mechanism and strategizing…

Transformer models rely on High-Performance Computing (HPC) resources for inference, where soft errors are inevitable in large-scale systems, making the reliability of the model particularly critical. Existing fault tolerance frameworks for…

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to co-channel interference. One possibility to address this issue is the use of orthogonal multiple access (OMA) techniques. However, due to a…

Sequence alignment is a memory bound computation whose performance in modern systems is limited by the memory bandwidth bottleneck. Processing-in-memory architectures alleviate this bottleneck by providing the memory with computing…

Deep reinforcement learning (DRL) has emerged as a powerful paradigm for solving complex decision-making problems. However, DRL-based systems still face significant dependability challenges particularly in real-time environments due to the…

All real time tasks which are termed as critical tasks by nature have to complete its execution before its deadline, even in presence of faults. The most popularly used real time task assignment algorithms are First Fit (FF), Best Fit (BF),…

Designing industrial systems, such as building, improving, and automating distribution centers and manufacturing plants, involves critical decision-making with limited information in the early phases. The lack of information leads to less…

Fault-tolerance has always been an important topic when it comes to running massively parallel programs at scale. Statistically, hardware and software failures are expected to occur more often on systems gathering millions of computing…

Generational improvements to commodity DRAM throughout half a century have long solidified its prevalence as main memory across the computing industry. However, overcoming today's DRAM technology scaling challenges requires new solutions…

Efficient memory management in heterogeneous systems is increasingly challenging due to diverse compute architectures (e.g., CPU, GPU, FPGA) and dynamic task mappings not known at compile time. Existing approaches often require programmers…

Resistive random-access memory (ReRAM)-based processing-in-memory (PIM) architecture is an attractive solution for training Graph Neural Networks (GNNs) on edge platforms. However, the immature fabrication process and limited write…

Remote direct memory access (RDMA) networks are being rapidly adopted into industry for their high speed, low latency, and reduced CPU overheads compared to traditional kernel-based TCP/IP networks. RDMA enables threads to access remote…