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Sparse matrix multiplication (SpMM) plays a critical role in high-performance computing applications, such as deep learning, image processing, and physical simulation. Field-Programmable Gate Arrays (FPGAs), with their configurable hardware resources, can ...

Benchmarks are essential in the design of modern HPC installations, as they define key aspects of system components. Beyond synthetic workloads, it is crucial to include real applications that represent user requirements into benchmark suites, to ...

FPGAs facilitate prototyping and debug, and recently accelerate full-stack simulations due to their rapid turnaround time (TAT). However, this TAT is restrictive in exhaustive design space explorations of parameterized RTL generators, especially ...

• Machine Learning.
• Accelerators.
• Systolic Array.
• Design Space Exploration.

The emergence of HBM as a high-volume memory product has made memory bandwidths of 1.2TB/s (1 stack) to 4.8TB/s (4 stacks) feasible. Exploiting such bandwidths requires high memory level parallelism, but the memory access mechanisms in today’s CPUs are ...

Lightweight convolutional neural networks (CNNs) enable lower inference latency and data traffic, facilitating deployment on resource-constrained edge devices such as field-programmable gate arrays (FPGAs). However, CNNs inference requires access ...

The deployment of Convolutional Neural Networks (CNNs) on edge devices has gradually become a hot topic in research and application. However, simply pursuing high-performance networks is no longer suitable for scenarios that require comprehensive ...

Neocortex[4] is a National Science Foundation (NSF)[8] system for artificial intelligence workflows that integrates hundreds of thousands of cores coupled with high-speed on-chip memory, making it ideal for complex AI tasks that require high throughput ...

Computing deep AI algorithms on traditional CPUs and GPUs brings several performance and energy pitfalls. Most of the emerging AI accelerators target only the inference phase of deep learning. There have been very limited attempts to design a full-...

Attention-based transformers have achieved significant performance breakthroughs in natural language processing (NLP) and computer vision (CV) tasks. Meanwhile, the ever-increasing length of today’s input sequences puts much pressure on computing ...

In the rapidly advancing landscape of computing, hardware accelerator designs are pivotal for satisfying high performance and low power demands. Systolic array (SA) architectures, tailored for general matrix multiplication (GEMM) operations, are ideal ...

WebAssembly is a promising bytecode virtualization technology for embedded systems. WebAssembly interpreters for embedded demonstrate strong isolation and portability. However, they come with a significant performance penalty compared to direct ...

The simulation of biological neural networks holds immense promise for advancing both neuroscience and artificial intelligence. Due to its high complexity, it requires powerful computers. However, the high proportion of communication and routing ...

This study proposes a new method for diagnosing Parkinson's disease using audio signals and FPGA-based convolutional neural networks. The proposed method involves training a convolutional neural network and using deep learning techniques to ...

With the recent advancements in high-performance computing, convolutional neural networks (CNNs) have achieved remarkable success in various vision tasks. However, along with improvements in model accuracy, the size and computational complexity of ...

Recently, the neural networks with convolution computation is widely used for image classification and recognition. For real-time implementation, the video buffer is required to store the image temperately. However, traditional buffers like CLSB (...

Ideally, accelerator development should be as easy as software development. Several recent design languages/tools are working toward this goal, but actually testing early designs on real applications end-to-end remains prohibitively difficult due to the ...

Coarse-Grained Reconfigurable Arrays (CGRA) are promising edge accelerators due to the outstanding balance in flexibility, performance, and energy efficiency. Classic CGRAs statically map compute operations onto the processing elements (PE) and route the ...

Accelerators, such as GPUs, SmartNICs and FPGAs, are common components of research systems today. This paper focuses on the question of how to fairly compare these systems. This is challenging because it requires comparing systems that use different ...

In recent years, we have seen an emergence of novel spatial architectures to accelerate domain-specific workloads like Machine Learning. There is a need to investigate their performance characteristics for traditional HPC workloads for their tighter ...

Convolutional neural networks (CNNs) are essential for advancing the field of artificial intelligence. However, since these networks are highly demanding in terms of memory and computation, implementing CNNs can be challenging. To make CNNs more ...