Related papers: Energy Efficiency of Many-Soft-Core Processors
Current soft processor architectures for FPGAs do not utilize the potential of the massive parallelism available. FPGAs now support many thousands of embedded floating point operators, and have similar computational densities to GPGPUs.…
The advent of heterogeneous multi-core architectures brought with it huge benefits to energy efficiency by running programs on properly-sized cores. Modern heterogeneous multi-core systems as suggested by Artjom et al. schedule tasks to…
The paradigm shift towards multi-core and heterogeneous computing, driven by the fundamental power and thermal limits of single-core processors, has established energy efficiency as a first-class design constraint in high-performance…
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…
We carry out a comparative performance study of multi-core CPUs, GPUs and Intel Xeon Phi (Many Integrated Core - MIC) with a microscopy image analysis application. We experimentally evaluate the performance of computing devices on core…
Modern multicore processors are employing large last-level caches, for example Intel's E7-8800 processor uses 24MB L3 cache. Further, with each CMOS technology generation, leakage energy has been dramatically increasing and hence, leakage…
In this treatise, my research on methods to improve efficiency, reliability, and security of reconfigurable hardware systems, i.e., FPGAs, through partial dynamic reconfiguration is outlined. The efficiency of reconfigurable systems can be…
The growing concern for energy efficiency in the Information and Communication Technology (ICT) sector has prompted the exploration of resource management techniques. While hardware architectures, such as single-ISA asymmetric multicore…
Recent years have witnessed a phenomenal growth in the computational capabilities and applications of GPUs. However, this trend has also led to dramatic increase in their power consumption. This paper surveys research works on analyzing and…
We investigate the energy efficiency of a library designed for parallel computations with sparse matrices. The library leverages high-performance, energy-efficient Graphics Processing Unit (GPU) accelerators to enable large-scale scientific…
Recent advances in soft GPGPU architectures have shown that a small (<10K LUT), high performance (770 MHz) processor is possible in modern FPGAs. In this paper we architect and evaluate soft SIMT processor banked memories, which can support…
When a single core is scaled up to m cores occupying the same chip area and executing the same (parallelizable) task, achievable speedup is square-root m, power is reduced by square-root m and energy is reduced by m. Thus, many-core…
In this work, we examine the performance, energy efficiency and usability when using Python for developing HPC codes running on the GPU. We investigate the portability of performance and energy efficiency between CUDA and OpenCL; between…
This paper presents a complete video fusion system with hardware acceleration and investigates the energy trade-offs between computing in the CPU or the FPGA device. The video fusion application is based on the Dual-Tree Complex Wavelet…
This paper introduces a computer architecture, where part of the instruction set architecture (ISA) is implemented on small highly-integrated field-programmable gate arrays (FPGAs). Small FPGAs inside a general-purpose processor (CPU) can…
The edge computing paradigm has emerged to handle cloud computing issues such as scalability, security and low response time among others. This new computing trend heavily relies on ubiquitous embedded systems on the edge. Performance and…
Massively parallel architectures offer the potential to significantly accelerate an application relative to their serial counterparts. However, not all applications exhibit an adequate level of data and/or task parallelism to exploit such…
Current computational systems are heterogeneous by nature, featuring a combination of CPUs and GPUs. As the latter are becoming an established platform for high-performance computing, the focus is shifting towards the seamless programming…
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…
With their widespread availability, FPGA-based accelerators cards have become an alternative to GPUs and CPUs to accelerate computing in applications with certain requirements (like energy efficiency) or properties (like fixed-point…