Related papers: OpenRISC System-on-Chip Design Emulation

This study presents the first implementation of multilayer neural networks on a memristor/CMOS integrated system on chip (SoC) to simultaneously detect multiple diseases. To overcome limitations in medical data, generative AI techniques are…

In this paper we deal with the impact of multi and many-core processor architectures on simulation. Despite the fact that modern CPUs have an increasingly large number of cores, most softwares are still unable to take advantage of them. In…

Modern SoC-FPGA that consists of FPGA with embedded ARM cores is being popularized as an embedded vision system platform. However, the design approach of SoC-FPGA applications still follows traditional hardware-software separate workflow,…

High-Performance Computing (HPC) processors are nowadays integrated Cyber-Physical Systems demanding complex and high-bandwidth closed-loop power and thermal control strategies. To efficiently satisfy real-time multi-input multi-output…

As the communication requirements of current and future Multiprocessor Systems on Chips (MPSoCs) continue to increase, scalable communication architectures are needed to support the heavy communication demands of the system. This is…

In this paper is proposed a technique to integrate and simulate a dynamic memory in a multiprocessor framework based on C/C++/SystemC. Using host machine's memory management capabilities, dynamic data processing is supported without…

Software-hardware co-design solutions for decimal computation can provide several Pareto points to development of embedded systems in terms of hardware cost and performance. This paper demonstrates how to accurately evaluate such co-design…

High Performance Computing (HPC) platforms allow scientists to model computationally intensive algorithms. HPC clusters increasingly use General-Purpose Graphics Processing Units (GPGPUs) as accelerators; FPGAs provide an attractive…

Approximate computing (AC) leverages the inherent error resilience and is used in many big-data applications from various domains such as multimedia, computer vision, signal processing, and machine learning to improve systems performance…

Modern chip design is complex, and there is a crucial need for early-stage prediction of key design-quality metrics like timing and routing congestion directly from Verilog code (a commonly used programming language for hardware design). It…

Memristors have shown promising features for enhancing neuromorphic computing concepts and AI hardware accelerators. In this paper, we present a user-friendly software infrastructure that allows emulating a wide range of neuromorphic…

Traditional machine learning depends on high-precision arithmetic and near-ideal hardware assumptions, which is increasingly challenged by variability in aggressively scaled semiconductor devices. Compute-in-memory (CIM) architectures…

Heterogeneity has been an indispensable aspect of distributed computing throughout the history of these systems. In particular, with the increasing prevalence of accelerator technologies (e.g., GPUs and TPUs) and the emergence of…

The Network on Chip (NoC) paradigm is rapidly replacing bus based System on Chip (SoC) designs due to their inherent disadvantages such as non-scalability, saturation and congestion. Currently very few tools are available for the simulation…

With the growth of embedded systems, VLSI design phases complexity and cost factors across the globe and has become outsourced. Modern computing ICs are now using system-on-chip for better on-chip processing and communication. In the era of…

The classical simulation of quantum algorithms is a crucial tool for circuit development, testing, and validation. Although acceleration using GPUs significantly reduces simulation time, most high-performance simulators rely on…

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…

Careful design of semiconductor manufacturing equipment is crucial for ensuring the performance, yield, and reliability of semiconductor devices. Despite this, numerical optimization methods are seldom applied to optimize the design of such…

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…

Simulation-guided design represents a fundamental contribution towards the development of modern semiconductor devices aiming to reach high-performance particle detection, identification and tracking, and constitutes a strategic element of…