Neuromorphic Computing

""Internet Protocol  Television  (IPTV)  has gained  an enormous growth rate by revolutionizing personal entertainment. High data rates with increased coverage radius of IEEE 802.11n Wireless Local Area Networks (WLANs) motivate  the concept of wireless IPTV.  Streaming  of Television  contents over  highly pervasive  wireless environment  with satisfactory Quality of Service (QoS)  is a challenging task. Focusing on wireless IPTV, our work deals with the capacity  evaluation  of IPTV  users over  IEEE  802.11n.  We  first present  an upper capacity  limit for supporting maximum number of users over  IEEE 802.11n. We  then propose that 4-times  packet  size is the optimal frame aggregation size  for  IPTV  which maximizes  users  capacity and QoS. Finally, we suggest  the use  of Datagram  Congestion Control  Protocol (DCCP) at transport layer for IPTV. We show that DCCP capacity for IPTV  increases upto 35% by reducing packet  losses at Access Point  (AP), compared to User Datagram Protocol (UDP). We further evaluate fairness of IPTV traffic in the presence of Transmission Control Protocol (TCP) traffic in the network. Our study concludes that IPTV using DCCP over IEEE  802.11n not only provides  increased user’s  capacity but also co-exists fairly with TCP traffic.

Internet Protocol Television (IPTV) and Voice over Internet Protocol (VoIP) have gained unprecedented growth rates in the past few years. Data rate and high coverage area of IEEE 802.11n motivate the concept of combined IPTV and VoIP over IEEE 802.11n. Transmission of combined IPTV and VoIP over a wireless network is a challenging task. In this paper, we deal with the capacity evaluation of combined IPTV and VoIP over IEEE 802.11n. We evaluate the use of Datagram Congestion Control Protocol (DCCP) at transport layer of IPTV and VoIP. Our study shows that DCCP can enhance capacity of IPTV by 25%. Our study confirms that performance of DCCP deteriorates severely in presence of any other UDP flow because of congestion-less mechanism of UDP. Our fairness analysis with TCP traffic shows that IPTV and VoIP using DCCP provides fair share in bandwidth to TCP with 19% increase in combined capacity. We study the effect of IEEE 802.11n parameters and obtain optimal values. We show the optimal values and trends of Access Point (AP) parameters including Queue size, Transmission Opportunity, Aggregation and Block ACK etc. Our study shows that nearly 9 more VoIP users are supported with a queue size of 70 packets and transmission opportunity of 9. Our study concludes that selection of DCCP and optimized parameters over IEEE 802.11n can enhance the capacity of IPTV and VoIP by atleast 25% and 19% respectively as compared to the use of UDP.

"Internet Protocol Television (IPTV) has gained an enormous growth rate by revolutionizing personal entertainment. High data rates with increased coverage radius of IEEE 802.11n Wireless Local Area Networks (WLANs) motivate the concept of wireless IPTV. Focusing on wireless IPTV, our work deals with the capacity evaluation of IPTV users over IEEE 802.11n. We first present an upper capacity limit for supporting maximum number of users over IEEE 802.11n. We then propose that 4-times packet size is the optimal frame aggregation size for IPTV which maximizes users capacity and QoS. Moreover, we suggest the use of Datagram Congestion Control Protocol (DCCP) at transport layer for IPTV. We show that DCCP increases capacity upto 35% for IPTV by reducing packet losses at Access Point (AP), compared to User Datagram Protocol (UDP). We further evaluate fairness of IPTV traffic in the presence of Transmission Control Protocol (TCP) traffic in the network. Our study concludes that IPTV using DCCP over IEEE 802.11n not only provides increased user’s capacity but also co-exists fairly with TCP traffic. Transmission of combined IPTV and VoIP over a wireless network is a challenging task. We evaluate capacity of combined IPTV and VoIP over IEEE 802.11n. We then evaluate the use of DCCP at transport layer of IPTV. Our study shows that DCCP(TFRC) can provide 25% additional IPTV users. Our results suggest that performance of DCCP is worst in presence of any other UDP flow because of congestion-less mechanism of UDP. Our fairness analysis with TCP traffic shows that IPTV and VoIP using TFRC provide fair share in bandwidth to TCP with 19% increase in combined capacity. We evaluate optimal parameters of IEEE 802.11n AP for running IPTV and VoIP. We show the optimal values and trends of AP parameters including queue size, transmission opportunity, aggregation and block acknowledgement etc. Our study shows that nearly 9 more VoIP users are supported with a queue size of 70 packets and transmission opportunity of 9. Our study concludes that selection of DCCP and optimized parameters over IEEE 802.11n can enhance capacity of IPTV and VoIP by at-least 19% than use of UDP."

Tremendous growth rates of Internet Protocol Television (IPTV) and Voice over Internet Protocol (VoIP) have demanded the shift of paradigm from wired to wireless applications. Increased packet loss with continuously varying wireless conditions make the transmission a challenging task in wireless environment. Our study investigates and proposes improvement in the transmission of combined IPTV and VoIP over the IEEE 802.11n WLAN. Our major contributions include the analytical and experimental investigations of (1) transport layer protocol UDP/TFRC for IPTV and VoIP, (2) optimal physical layer parameters for IPTV and VoIP, (3) proposition of wireless enhancement of TFMCC (W-TFMCC) to enhance the capacity and Quality of Service (QoS) of wireless IPTV and VoIP. Analytical and experimental evaluations show a 25% increase in capacity using TFRC with 167% more bandwidth share to TCP. Our study shows that use ofW-TFMCC with optimal parameters can enhance IPTV and VoIP capacity by 44%.

The Internet relies heavily on programmable matchaction processors for matching network packets against locally available network rules and taking actions, such as forwarding and modification of network packets. This match-action process must be performed at high speed, i.e., commonly within one clock cycle, using a specialized memory unit called Ternary Content Addressable Memory (TCAM). Building on transistor-based CMOS designs, state-of-the-art TCAM architectures have high energy consumption and lack resilient designs for incorporating novel technologies for performing appropriate actions. In this article, we motivate the use of a novel fundamental component, the 'Memristor', for the development of TCAM architecture for match-action processing. Memristors can provide energy efficiency, non-volatility and better resource density as compared to transistors. We have proposed a novel memristorbased TCAM architecture called TCAmM CogniGron , built upon the voltage divider principle and requiring only two memristors and five transistors for storage and search operations compared to sixteen transistors in the traditional TCAM architecture. We analyzed its performance over an experimental data set of Nbdoped SrTiO3-based memristor. The analysis of TCAmMCogniGron showed promising power consumption statistics of 16 µW and 1 µW for match and mismatch operations along with twice the improvement in resources density as compared to the traditional architectures.

Match-action processors play a crucial role of communicating end-users in the Internet by computing network paths and enforcing administrator policies. The computation process uses a specialized memory called Ternary Content Addressable Memory (TCAM) to store processing rules and use header information of network packets to perform a match within a single clock cycle. Currently, TCAM memories consume huge amounts of energy resources due to the use of traditional transistor-based CMOS technology. In this article, we motivate the use of a novel component, the memristor, for the development of a TCAM architecture. Memristors can provide energy efficiency, nonvolatility, and better resource density as compared to transistors. We have proposed a novel memristor-based TCAM architecture built upon the voltage divider principle for energy efficient match-action processing. Moreover, we have tested the performance of the memristor-based TCAM architecture using the experimental data of a novel Nb-doped SrTiO3 memristor. Energy analysis of the proposed TCAM architecture for given memristor shows promising power consumption statistics of 16 µW for a match operation and 1 µW for a mismatch operation.

The high performance requirements of nowadays computer networks are limiting their ability to support important requirements of the future. Two important properties essential in assuring cost-efficient computer networks and supporting new challenging network scenarios are operating energy efficient and supporting cognitive computational models. These requirements are hard to fulfill without challenging the current architecture behind network packet processing elements such as routers and switches. Notably, these are currently dominated by the use of traditional transistor-based components. In this article, we contribute with an in-depth analysis of alternative architectural design decisions to improve the energy footprint and computational capabilities of future network packet processors by shifting from transistor-based components to a novel component named Memristor. A memristor is a computational component characterized by nonvolatile operations on a physical state, mostly represented in form of (electrical) resistance. Its state can be read or altered by input signals, e.g. electrical pulses, where the future state always depends on the past state. Unlike in traditional von Neumann architectures, the principles behind memristors impose that memory operations and computations are inherently colocated. In combination with the non-volatility, this allows to build memristors at nanoscale size and significantly reduce the energy consumption. At the same time, memristors appear to be highly suitable to model cognitive functionality due to the state dependence transitions in the memristor. In cognitive architectures, our survey contributes to the study of memristor-based Ternary Content Addressable Memory (TCAM) used for storage of cognitive rules inside packet processors. Moreover, we analyze the memristor-based novel cognitive computational architectures built upon self-learning capabilities by harnessing from non-volatility and state-based response of memristors (including reconfigurable architectures, reservoir computation architectures, neural network architectures and neuromorphic computing architectures).