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Serverless computing is gaining traction as an attractive model for the deployment of a multitude of workloads in the cloud. Designing and building effective resource management solutions for any computing environment requires extensive…
The rapid growth of AI applications has driven increased demand for specialized AI hardware, highlighting critical opportunities within the memory subsystem, which often serves as a performance bottleneck in high-demand workloads such as…
The miniaturization of transistors down to 5nm and beyond, plus the increasing complexity of integrated circuits, significantly aggravate short channel effects, and demand analysis and optimization of more design corners and modes.…
The substantial memory requirements of Large Language Models (LLMs), particularly for long-context fine-tuning, have renewed interest in CPU offloading to augment limited GPU memory. However, as context lengths grow, relying on CPU memory…
The memory system is a major performance determinant for server processors. Ever-growing core counts and datasets demand higher bandwidth and capacity as well as lower latency from the memory system. To keep up with growing demands,…
Efficiently serving Large Language Models (LLMs) requires selecting an optimal parallel execution plan, balancing computation, memory, and communication overhead. However, determining the best strategy is challenging due to varying…
Nowadays simulations can produce petabytes of data to be stored in parallel filesystems or large-scale databases. This data is accessed over the course of decades often by thousands of analysts and scientists. However, storing these volumes…
Custom memory organization are challenging task in the area of VLSI design. This study aims to design high speed and low power consumption memory for embedded system. Synchronous SRAM has been proposed and analyzed using various simulators.…
Cycle-accurate simulators are widely used to study systolic accelerators, yet their accuracy and usability are often limited by weak validation against real hardware and poor integration with modern ML compiler stacks. This paper presents…
Storage-class memory (SCM) combines the benefits of a solid-state memory, such as high-performance and robustness, with the archival capabilities and low cost of conventional hard-disk magnetic storage. Among candidate solid-state…
Solid-state storage architectures based on NAND or emerging memory devices (SSD), are fundamentally architected and optimized for both reliability and performance. Achieving these simultaneous goals requires co-design of memory components…
The rapid advancements in AI, scientific computing, and high-performance computing (HPC) have driven the need for versatile and efficient hardware accelerators. Existing tools like SCALE-Sim v2 provide valuable cycle-accurate simulations…
With the increasing computations in power system simulations, high-performance and cost-effective power system simulator is highly required. In this paper, a cloud-computing based power system simulator, namely CloudPSS, is designed. Based…
The development of cost-effective highperformance parallel computing on multi-processor supercomputers makes it attractive to port excessively time consuming simulation software from personal computers (PC) to super computes. The power…
Cloud Data centers aim to provide reliable, sustainable and scalable services for all kinds of applications. Resource scheduling is one of keys to cloud services. To model and evaluate different scheduling policies and algorithms, we…
Hybrid Solid-State Drives (SSDs), which integrate several types of flash cells (e.g., single-level cell (SLC) and multiple-level cell (MLC)) in a single drive and enable them to convert between each other, are designed to deliver both high…
PCIe-connected FPGAs are gaining popularity as an accelerator technology in data centers. However, it is challenging to jointly develop and debug host software and FPGA hardware. Changes to the hardware design require a time-consuming FPGA…
Recently, there has been an extensive research effort in building efficient large language model (LLM) inference serving systems. These efforts not only include innovations in the algorithm and software domains but also constitute…
Large Language Model (LLM) inference requires substantial computational resources, yet CPU-based inference remains essential for democratizing AI due to the widespread availability of CPUs compared to specialized accelerators. However,…
As the capacity of Solid-State Drives (SSDs) is constantly being optimised and boosted with gradually reduced cost, the SSD cluster is now widely deployed as part of the hybrid storage system in various scenarios such as cloud computing and…