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Whilst RISC-V has grown phenomenally quickly in embedded computing, it is yet to gain significant traction in High Performance Computing (HPC). However, as we move further into the exascale era, the flexibility offered by RISC-V has the…

In this paper, we present Ara, a 64-bit vector processor based on the version 0.5 draft of RISC-V's vector extension, implemented in GlobalFoundries 22FDX FD-SOI technology. Ara's microarchitecture is scalable, as it is composed of a set of…

The posit representation for real numbers is an alternative to the ubiquitous IEEE 754 floating-point standard. In this work, we present PERCIVAL, an application-level posit capable RISC-V core based on CVA6 that can execute all posit…

Heterogeneous, multicore SoC architectures are a critical component of today's computing landscape. However, supporting both increasing heterogeneity and multicore execution are significant design challenges. Meanwhile, the growing RISC-V…

Deploying deep neural networks (DNNs) on those resource-constrained edge platforms is hindered by their substantial computation and storage demands. Quantized multi-precision DNNs, denoted as MP-DNNs, offer a promising solution for these…

The RISC-V Instruction Set Architecture (ISA) has enjoyed phenomenal growth in recent years, however it still to gain popularity in HPC. Whilst adopting RISC-V CPU solutions in HPC might be some way off, RISC-V based PCIe accelerators offer…

Computing-in-memory (CIM) is renowned in deep learning due to its high energy efficiency resulting from highly parallel computing with minimal data movement. However, current SRAM-based CIM designs suffer from long latency for loading…

Energy efficiency is one of the major concern in designing advanced computing infrastructures. From single nodes to large-scale systems (data centers), monitoring the energy consumption of the computing system when applications run is a…

The exponential emergence of Field Programmable Gate Array (FPGA) has accelerated the research of hardware implementation of Deep Neural Network (DNN). Among all DNN processors, domain specific architectures, such as, Google's Tensor…

Spiking Neural Networks (SNNs) have the potential to drastically reduce the energy requirements of AI systems. However, mainstream accelerators like GPUs and TPUs are designed for the high arithmetic intensity of standard ANNs so are not…

Cryptographic algorithms are computationally costly and the challenge is more if we need to execute them in resource constrained embedded systems. Field Programmable Gate Arrays (FPGAs) having programmable logic de- vices and processing…

Real-time systems, particularly those used in domains like automated driving, are increasingly adopting neural networks. From this trend arises the need for high-performance hardware exhibiting predictable timing behavior. While…

Multi-core vector processor architectures excel in handling computationally intensive vectorizable tasks but struggle to achieve optimal resource utilization when facing sequential and control tasks that cannot be vectorized. This work…

As RISC-V architectures proliferate across embedded and high-performance domains, developers face persistent challenges in performance optimization due to fragmented tooling, immature hardware features, and platform-specific defects. This…

Spiking Neural Networks (SNNs) have gained significant attention in edge computing due to their low power consumption and computational efficiency. However, existing implementations either use conventional System on Chip (SoC) architectures…

This paper presents EPAC, a RISC-V-based accelerator chip developed within the European Processor Initiative (EPI) as part of a multi-year, multi-partner effort to build a European HPC processor ecosystem. EPAC is implemented in…

Virtualization is a key technology used in a wide range of applications, from cloud computing to embedded systems. Over the last few years, mainstream computer architectures were extended with hardware virtualization support, giving rise to…

Neuromorphic computing, inspired by biological neural systems, holds immense promise for ultra-low-power and real-time inference applications. However, limited access to flexible, open-source platforms continues to hinder widespread…

The rapid advancement of AI workloads and domain-specific architectures has led to increasingly diverse processor microarchitectures, whose design exploration requires fast and accurate performance validation. However, traditional workflows…

This article presents an asynchronous FPGA architecture for implementing cryptographic algorithms secured against physical cryptanalysis. We discuss the suitability of asynchronous reconfigurable architectures for such applications before…