دانلود مقاله ISI انگلیسی شماره 63041
ترجمه فارسی عنوان مقاله

برآورد عملکرد / قیمت برای سخت افزار در مقیاس قشر: اکتشاف فضای طراحی

عنوان انگلیسی
Performance/price estimates for cortex-scale hardware: A design space exploration
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
63041 2011 14 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Neural Networks, Volume 24, Issue 3, April 2011, Pages 291–304

ترجمه کلمات کلیدی
مجازیسازی زمان بندی سازی نورون اسپایکینگ، گره پردازش، سخت افزار، دیجیتال، مخلوط سیگنال، کارایی، قیمت، بهره وری، فضای طراحی، سخت افزار طیف
کلمات کلیدی انگلیسی
Virtualization; Time-multiplexing; Spiking neuron; Processing node; Hardware; Digital; Mixed-signal; Performance; Price; Efficiency; Design-space; Hardware-spectrum

چکیده انگلیسی

In this paper, we revisit the concept of virtualization. Virtualization is useful for understanding and investigating the performance/price and other trade-offs related to the hardware design space. Moreover, it is perhaps the most important aspect of a hardware design space exploration. Such a design space exploration is a necessary part of the study of hardware architectures for large-scale computational models for intelligent computing, including AI, Bayesian, bio-inspired and neural models. A methodical exploration is needed to identify potentially interesting regions in the design space, and to assess the relative performance/price points of these implementations. As an example, in this paper we investigate the performance/price of (digital and mixed-signal) CMOS and hypothetical CMOL (nanogrid) technology based hardware implementations of human cortex-scale spiking neural systems. Through this analysis, and the resulting performance/price points, we demonstrate, in general, the importance of virtualization, and of doing these kinds of design space explorations. The specific results suggest that hybrid nanotechnology such as CMOL is a promising candidate to implement very large-scale spiking neural systems, providing a more efficient utilization of the density and storage benefits of emerging nano-scale technologies. In general, we believe that the study of such hypothetical designs/architectures will guide the neuromorphic hardware community towards building large-scale systems, and help guide research trends in intelligent computing, and computer engineering.