Impact of steep-slope transistors on non-von Neumann architectures: CNN case study

Palit, Indranil; Sedighi, Behnam; Horváth, András; Hu, Xiaobo Sharon; Nahas, Joseph; Niemier, Michael

doi:10.7873/date.2014.150

Cited by 6 publications

(10 citation statements)

References 13 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous research [9,10,11,12] have shown positive results by utilizing emerging devices in multiple CNN contexts. Specifically, [9] shows TFETs can be utilized as nonlinear resistive element in CNNs to improve power efficiency without sacrificing performance.…”

Section: Introductionmentioning

confidence: 93%

See 1 more Smart Citation

TFET-based Operational Transconductance Amplifier Design for CNN Systems

Lou

Palit

Horváth

et al. 2015

Proceedings of the 25th Edition on Great Lakes Symposium on VLSI

Self Cite

View full text Add to dashboard Cite

A Cellular Neural Network (CNN) is a powerful processor that can significantly improve the performance of spatiotemporal applications such as pattern recognition, image processing, motion detection, when compared to the more traditional von Neumann architecture. In this paper, we show how tunneling field effect transistors (TFETs) can be utilized to enhance the performance of CNNs. Specifically, power consumption of TFET-based CNNs can be significantly lower when compared to MOSFET-based CNNs due to improved voltage controlled current sources (VCCSs) -an important component in CNN systems. We demonstrate that CNNs can benefit from low power conventional linear VCCSs implemented via TFETs. We also show that TFETs can be useful to realize non-linear VCCSs, which are either not possible or exhibit degraded performance when implemented via CMOS. Such non-linear VCCSs help to improve the performance of certain CNN operations (e.g., global maximum/minimum). We provide two case studiesimage contrast enhancement and maximum row selectionthat illustrate the benefits of non-linear VCCSs (e.g., reduced computation time, energy dissipation, etc.) when compared to CMOS-based approaches.

show abstract

Section: Introductionmentioning

confidence: 93%

“…Specifically, [9] shows TFETs can be utilized as nonlinear resistive element in CNNs to improve power efficiency without sacrificing performance. [10] further uses TFETs to construct circuits to realize ternary outputs.…”

Section: Introductionmentioning

confidence: 99%

TFET-based Operational Transconductance Amplifier Design for CNN Systems

Lou

Palit

Horváth

et al. 2015

Proceedings of the 25th Edition on Great Lakes Symposium on VLSI

Self Cite

View full text Add to dashboard Cite

show abstract

“…2. (a) Example characteristics of a CNN device for multi-valued problems; (b) I-V characteristics of the double-layer graphene device at room temperature [15], and its Piece-wise linear (PWL) approximation [5].…”

Section: A Cnn Fundamentalsmentioning

confidence: 99%

“…2b) might be able to deliver the desired characteristics illustrated in Fig. 2a. (See [5], [6] for more detail.) However, multi-valued characteristics can also be realized via circuits based on TFETs.…”

Section: A Cnn Fundamentalsmentioning

confidence: 99%

“…Given the above -and considering that the performance/energy benefits from more traditional technology scaling and core scaling are stagnating [3], [4] -there has been renewed and growing interest in nonvon Neumann and non-Boolean computer architectures. We expand upon our prior work [5], [6] to consider how emerging transistor technologies might impact the performance, energy, and program complexity of CNNs. A particular emphasis of our work is emerging transistor technologies being developed under the umbrella of SRC/DARPA's STARnet initiative's Low Energy Systems Technology (LEAST) center.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Architectural impacts of emerging transistors

Horváth

Nahas

et al. 2014

2014 IEEE 12th International New Circuits and Systems Conference (NEWCAS)

View full text Add to dashboard Cite

At present there is much effort to determine if emerging information processing devices could have a positive impact on the performance of non-Boolean/non-von Neumann computer architectures. We explore this topic here by specifically considering how emerging transistor technologies might impact cellular neural networks (CNNs). For CNNs, prior work suggests that new transistor structures could be employed to better facilitate non-binary outputs -that in turn reduce the number of template/programming operations as well as the hardware paths needed to solve a given problem. Here, we present analysis that considers how the above approach could impact the performance/energy of the cells that comprise the CNN. We also consider how characteristics of other emerging transistor technologies could positively impact CNNs -particularly with respect to reduced program complexity.

show abstract

An overview memristor based hardware accelerators for deep neural network

Gökgöz,

Gül,

Aydın

2024

Concurrency and Computation

View full text Add to dashboard Cite

The prevalence of artificial intelligence applications using artificial neural network architectures for functions such as natural language processing, text prediction, object detection, speech, and image recognition has significantly increased in today's world. The computational functions performed by artificial neural networks in classical applications require intensive and large‐scale data movement between memory and processing units. Various software and hardware efforts are being made to perform these operations more efficiently. Despite these efforts, latency in data traffic and the substantial amount of energy consumed in data processing emerge as bottleneck disadvantages of the Von Neumann architecture. To overcome this bottleneck problem, it is necessary to develop hardware units specific to artificial intelligence applications. For this purpose, neuro‐inspired computing chips are believed to provide an effective approach by designing and integrating a set of features inspired by neurobiological systems at the hardware level to address the problems arising in artificial intelligence applications. The most notable among these approaches is memristor‐based neuromorphic computing systems. Memristors are seen as promising devices for hardware‐level improvement in terms of speed and energy because they possess non‐volatile memory and exhibit analog behavior. They enable effective storage and processing of synaptic weights, offering solutions for hardware‐level development. Taking into account these advantages of memristors, this study examines the research conducted on artificial neural networks and hardware that can directly perform deep learning functions and mimic the biological brain, which is different from classical systems in today's context.

show abstract

Impact of steep-slope transistors on non-von Neumann architectures: CNN case study

Cited by 6 publications

References 13 publications

TFET-based Operational Transconductance Amplifier Design for CNN Systems

TFET-based Operational Transconductance Amplifier Design for CNN Systems

Architectural impacts of emerging transistors

An overview memristor based hardware accelerators for deep neural network

Contact Info

Product

Resources

About