Low power and high speed current-mode memristor-based TLGs

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

et al. 2018

With the recent advances of the emerging memories technologies, research are able to implement novel circuits, systems and computer architectures towards the design of high-performance and low-power electronic systems able to accelerate and/or optimize the functionality of many computer workflows. One emerging technology, the ReRAM/memristor is gathering attention due to its inherent advantages for logic and memory computing systems. At the same time, CMOS circuit design seems to have reached a limit, where easily optimized circuit solutions cannot be found. Thus, further research towards novel logic gate families, such as Threshold Logic Gates (TLGs), a logic family known for its high-speed and low power consumption, is needed. Although many implementation concepts of TLG circuit are using memristors, few of these implementations are based on physical ReRAM devices. In this work we are proposing a memristor-based threshold logic gate design towards the optimization of computer workflows. The presented results include a physical implementation of the proposed circuits which supports the concept of memory-based reconfigurable computing circuits and systems.

Section: Theory a Circuit Designmentioning

confidence: 99%

Section: Theory a Circuit Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Metal Oxide-enabled Reconfigurable Memristive Threshold Logic Gates

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

et al. 2018

“…2a shows a memristor-enhanced current-mode TLG design. The differential branches consist of parallel memristively source-degenerated pMOS transistors (1T1M) [14].Memristors are employed as the analogue weights at the input/bias binary signals. Using memristors has the advantage of introducing highly localised, continuously tuneable, minimal front-end footprint and low-voltage operated memory into the TLG, thus providing a decisive advantage in the implementation of memory-heavy ANN accelerators [15].…”

Section: A Threshold Logic Gatesmentioning

confidence: 99%

Processing big-data with Memristive Technologies: Splitting the Hyperplane Efficiently

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

et al. 2018

An important cornerstone of data processing is the ability to efficiently capture structure in data. This entails treating the input space as a hyperplane that needs partitioning. We argue that several modern electronic systems can be understood as carrying out such partitionings: from standard logic gates to Artificial Neural Networks (ANNs). More recently, memristive technologies equipped such systems with the benefit of continuous tunability directly in hardware, thus rendering these reconfigurable in a power and space efficient manner. Here, we demonstrate several proof-of-concept examples where memristors enable circuits optimised to carry out different flavours of the fundamental task of splitting the hyperplane. These include threshold logic and receptive field based classifiers that are presented within the context of a unified perspective.

“…However the majority of available TLG designs are based on transistor networks 11,17,18 . Recently, the use of Current-Mode (CM) Differential TLGs seems to gaining ground as one of the fastest and low-power differential threshold logic (TL) implementaions 11,19 .…”

mentioning

confidence: 99%

Practical Implementation of Memristor-Based Threshold Logic Gates

IEEE Trans. Circuits Syst. I

et al. 2019

Current advances in emerging memory technologies enable novel and unconventional computing architectures for high-performance and low-power electronic systems, capable of carrying out massively parallel operations at the edge. One emerging technology, ReRAM, also known to belong in the family of memristors (memory resistors), is gathering attention due to its attractive features for logic and in-memory computing; benefits which follow from its technological attributes, such as nanoscale dimensions, low power operation and multi-state programming. At the same time, design with CMOS is quickly reaching its physical and functional limitations, and further research towards novel logic families, such as Threshold Logic Gates (TLGs) is scoped. TLGs constitute a logic family known for its high-speed and low power consumption, yet rely on conventional transistor technology. Introducing memristors enables a more affordable reconfiguration capability of TLGs. Through this work, we are introducing a physical implementation of a memristor-based currentmode TLG (MCMTLG) circuit and validate its design and operation through multiple experimental setups. We demonstrate 2-input and 3-input MCMTLG configurations and showcase their reconfiguration capability. This is achieved by varying memristive weights arbitrarily for shaping the classification decision boundary, thus showing promise as an alternative hardware-friendly implementation of Artificial Neural Networks (ANNs). Through the employment of real memristor devices as the equivalent of synaptic weights in TLGs, we are realizing components that can be used towards an in-silico classifier.Today's conventional computing paradigm is based on the MOSFET transistor and CMOS technology; two cornerstones which have underpinned the development of digital electronics over the last 5 decades. Although there is still optimism for future improvement of CMOS, accumulating scientific evidence indicates the need for advances in both new emerging technologies to replace MOSFETs and in new computer circuits and architectures 1,2 . The former addresses the increasing difficulty of pursuing further downscaling (with its associated drop in reliability 2 ) whilst the latter seeks to address the Von Neumann bottleneck, where increasingly big memories and powerful processors struggle to communicate over a limited interlink whose data transfer capacity doesn't scale fast enough 2-4 .On the computation/architecture front, there has been a sustained effort to develop bio-inspired computation concepts, mostly in the guise of artificial neural network-enabled (ANN) systems. Research on artificial neural networks has thus far spanned the entire interval between the first simplified models of all-or-none hardware neurons 5 and the current state-of-the-art GPU-based ANNs 6-8 . However, one often overlooked example of ANN-like computation can be found in the form of its quantized, digital counterpart, the so-called threshold logic (TL). TL is a model for performing a