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Embedded systems are parts of our daily life and used in many fields. They can be found in smartphones or in modern cars including GPS, light/rain sensors and other electronic assistance mechanisms. These systems may handle sensitive data…
The rapid advancements in artificial intelligence (AI), particularly the Large Language Models (LLMs), have profoundly affected our daily work and communication forms. However, it is still a challenge to deploy LLMs on resource-constrained…
Conventional wisdom holds that an efficient interface between an OS running on a CPU and a high-bandwidth I/O device should use Direct Memory Access (DMA) to offload data transfer, descriptor rings for buffering and queuing, and interrupts…
In recent years, Field-Programmable Gate Arrays (FPGA) have evolved rapidly paving the way for a whole new range of computing paradigms. On the other hand, computer applications are evolving. There is a rising demand for a system that is…
Fully Homomorphic Encryption (FHE) is known to be extremely computationally-intensive, application-specific accelerators emerged as a powerful solution to narrow the performance gap. Nonetheless, due to the increasing complexities in FHE…
The introduction of complex SoCs with multiple processor cores presents new development challenges, such that development support is now a decisive factor when choosing a System-on-Chip (SoC). The presented developments support strategy…
Fully Homomorphic Encryption (FHE) allows computing on encrypted data, enabling secure offloading of computation to untrusted serves. Though it provides ideal security, FHE is expensive when executed in software, 4 to 5 orders of magnitude…
To enhance the quality and speed of data processing and protect the privacy and security of the data, edge computing has been extensively applied to support data-intensive intelligent processing services at edge. Among these data-intensive…
Recent advances in reprogrammable hardware (e.g., FPGAs) and memory technology (e.g., DDR4, HBM) promise to solve performance problems inherent to graph processing like irregular memory access patterns on traditional hardware (e.g., CPU).…
Computational storage, known as a solution to significantly reduce the latency by moving data-processing down to the data storage, has received wide attention because of its potential to accelerate data-driven devices at the edge. To meet…
Disaggregating memory from compute offers the opportunity to better utilize stranded memory in cloud data centers. It is important to cache data in the compute nodes and maintain cache coherence across multiple compute nodes. However, the…
FPGA-based hardware accelerators have received increasing attention mainly due to their ability to accelerate deep pipelined applications, thus resulting in higher computational performance and energy efficiency. Nevertheless, the amount of…
There is a growing call for greater amounts of increasingly agile computational power for edge and cloud infrastructure to serve the computationally complex needs of ubiquitous computing devices. Thus, an important challenge is addressing…
Modern computing is shifting from homogeneous CPU-centric systems to heterogeneous systems with closely integrated CPUs and GPUs. While the CPU software stack has benefited from decades of memory safety hardening, the GPU software stack…
Fully Homomorphic Encryption (FHE) enables computation directly on encrypted data but incurs massive computational and memory overheads, often exceeding plaintext execution by several orders of magnitude. While custom ASIC accelerators can…
In recent years, the widespread informatization and rapid data explosion have increased the demand for high-performance heterogeneous systems that integrate multiple computing cores such as CPUs, Graphics Processing Units (GPUs),…
Heterogeneous systems consisting of general-purpose processors and different types of hardware accelerators are becoming more and more common in HPC systems. Especially FPGAs provide a promising opportunity to improve both performance and…
The AMD MI300A APU integrates CDNA3 GPUs with high-bandwidth memory and advanced accelerator features: FP8 matrix cores, asynchronous compute engines (ACE), and 2:4 structured sparsity. These capabilities are increasingly relied upon by…
In-Network Collective (INC) acceleration holds immense potential for optimizing AI training and inference; however, its cross-layer nature has historically hindered investment and adoption within the open Ethernet ecosystem. To bridge this…
The increasing growth of applications' memory capacity and performance demands has led the CPU vendors to deploy heterogeneous memory systems either within a single system or via disaggregation. For instance, systems like Intel's Knights…