Stochastic Computing Architectures for Information Processing Systems

Τέμενος, Νικόλαος; Nikolaos, Temenos

dc.contributor.author	Τέμενος, Νικόλαος	el
dc.contributor.author	Nikolaos, Temenos	en
dc.date.accessioned	2023-03-20T08:46:08Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/57248
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.24946
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Stochastic Computing	en
dc.subject	Digital Circuits and Systems	el
dc.subject	Stochastic Finite-State Machine	el
dc.subject	Markov Chain	el
dc.title	Stochastic Computing Architectures for Information Processing Systems	en
heal.type	doctoralThesis
heal.classification	Electrical and Computer Engineering	en
heal.dateAvailable	2024-03-19T22:00:00Z
heal.language	en
heal.access	embargo
heal.recordProvider	ntua	el
heal.publicationDate	2022-12-13
heal.abstract	Arithmetic operations on stochastic sequences is the basis of the unconventional computational approach known as Stochastic Computing (SC). Deviating from the standard binary arithmetic, SC encodes and processes the value of binary numbers in the form of stochastic sequences, making arithmetic operations and highly-complex functions realizable using a few simple standard logic gates and memory elements, having inherent natural robustness in soft-errors. SC’s properties and advantages have been exploited in a plethora of fields characterized by massive parallelism requirements like Neural Networks and Image Processing. Beyond its strong points, SC introduces an accuracy-latency trade-off impacting the energy efficiency. Therefore, achieving low latency along with increased computational accuracy is the primary design goal is SC systems. This dissertation presents novel SC architectures realizing essential arithmetic operations and nonlinear functions, as well as realistic Neural Networks and Image Processing applications based on them. In the first part of the dissertation, the operating principles of the architectures are introduced and their behavior is modeled based on Stochastic Finite-State Machines (SFSM) and analyzed using Markov Chains (MC). This leads to a deeper understanding of their stochastic dynamics and the verification of their proper operation. The MC modeling is further extended to a general methodology enabling the analytical derivation of the SFSMs. first and second moment statistical properties. The methodology is accompanied by overflow/underflow MC modeling, allowing to balance the accuracy-latency trade-off according to performance requirements, and to set the guidelines for the selection of the register’s size. In the second part of the dissertation, the proposed architectures are compared to existing ones, in the SC literature, in computational accuracy and hardware resources, including area, power and energy consumption as well as in terms of their advantages in the overall design flow. The efficacy of the architectures is demonstrated by using them as building blocks in the realization of several Digital Signal Processing (DSP) operations, including convolution, noise reduction and image down-sampling filters as well as Neural Networks. Finally, the results of the introduced architectures’ performance in computational accuracy and hardware resources are compared to those achieved using standard binary computing methods highlighting the advantages of the first ones.	en
heal.sponsor	The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number:1216)	en
heal.advisorName	Σωτηριάδης Παύλος-Πέτρος	el
heal.advisorName	Sotiriadis, Paul	en
heal.committeeMemberName	Panagopoulos, Athanasios
heal.committeeMemberName	Pekmestzi, Kiamal	en
heal.committeeMemberName	Varvarigou, Theodora	en
heal.committeeMemberName	Psarrakos, Panayiotis	en
heal.committeeMemberName	Doulamis, Anastasios	en
heal.committeeMemberName	Doulamis, Nikolaos	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	147 σ.	el
heal.fullTextAvailability	false