Fault-tolerance techniques for hybrid CMOS/nanoarchitecture

The authors propose two fault-tolerance techniques for hybrid CMOS/nanoarchitecture implementing logic functions as look-up tables. The authors compare the efficiency of the proposed techniques with recently reported methods that use single coding schemes in tolerating high fault rates in nanoscale fabrics. Both proposed techniques are based on error correcting codes to tackle different fault rates. In the first technique, the authors implement a combined two-dimensional coding scheme using Hamming and Bose-Chaudhuri-Hocquenghem (BCH) codes to address fault rates greater than 5. In the second technique, Hamming coding is complemented with bad line exclusion technique to tolerate fault rates higher than the first proposed technique (up to 20). The authors have also estimated the improvement that can be achieved in the circuit reliability in the presence of Don-t Care Conditions. The area, latency and energy costs of the proposed techniques were also estimated in the CMOS domain

Cell Architecture for Nanoelectronic Design

Nanoarchitectures, fault-tolerance, defect-tolerance, noise-tolerance, averaging cellSeveral nanoelectronic devices have been already proved. However, no architecture which makes use of them provides a feasible opportunity to build medium/large systems. Nanoarchitecture proposals only solve a small part of the problems needed to achieve a real design. In this paper, we propose and analyze a cell architecture that overcomes most of those at the gate level. Using the cell structure we build 2 and 3-input NAND gates showing their error probabilities. Finally, we outline a method to further improve the structure’s tolerance by taking advantage of interferences among nanodevices. Using this improvement we show that it is possible to reduce the output standard deviation by a factor larger than \u81ãN and restitute the signal levels using nanodevices

Designing a Fault Tolerant Neural Network Computing System Based On Nanoscale Electronic Elements

This article observes the potential for building neural network computing systems designed via use of nanoscale electronic elements. The theory of interrelation between the fault-tolerance index of such systems and its predetermining factors has been systematized. We have also developed an approach to analysis of properties of parallel computing systems including nanoscale electronic elements at the stage of designing computing systems for the purpose of providing the maximum fault-tolerance index. By means of computer-generated simulation, we have experimentally tested this approach and it has proved to be superior to the available methods for solving this particular problem.The reported study was funded by RFBR, according to the research project No. 16-37-60061 mol_а_dk

Enabling Design and Simulation of Massive Parallel Nanoarchitectures

A common element in emerging nanotechnologies is the increasing complex- ity of the problems to face when attempting the design phase, because issues related to technology, specific application and architecture must be evalu- ated simultaneously. In several cases faced problems are known, but require a fresh re-think on the basis of different constraints not enforced by standard design tools. Among the emerging nanotechnologies, the two-dimensional structures based on nanowire arrays is promising in particular for massively parallel architec- tures. Several studies have been proposed on the exploration of the space of architectural solutions, but only a few derived high-level information from the results of an extended and reliable characterization of low-level structures. The tool we present is of aid in the design of circuits based on nanotech- nologies, here discussed in the specific case of nanowire arrays, as best candi- date for massively parallel architectures. It enables the designer to start from a standard High-level Description Languages (HDL), inherits constraints at physical level and applies them when organizing the physical implementation of the circuit elements and of their connections. It provides a complete simu- lation environment with two levels of refinement. One for DC analysis using a fast engine based on a simple switch level model. The other for obtaining transient performance based on automatic extraction of circuit parasitics, on detailed device (nanowire-FET) information derived by experiments or by existing accurate models, and on spice-level modeling of the nanoarray. Re- sults about the method used for the design and simulation of circuits based on nanowire-FET and nanoarray will be presente

Hardware Architectures and Implementations for Associative Memories : the Building Blocks of Hierarchically Distributed Memories

During the past several decades, the semiconductor industry has grown into a global industry with revenues around $300 billion. Intel no longer relies on only transistor scaling for higher CPU performance, but instead, focuses more on multiple cores on a single die. It has been projected that in 2016 most CMOS circuits will be manufactured with 22 nm process. The CMOS circuits will have a large number of defects. Especially when the transistor goes below sub-micron, the original deterministic circuits will start having probabilistic characteristics. Hence, it would be challenging to map traditional computational models onto probabilistic circuits, suggesting a need for fault-tolerant computational algorithms. Biologically inspired algorithms, or associative memories (AMs)—the building blocks of cortical hierarchically distributed memories (HDMs) discussed in this dissertation, exhibit a remarkable match to the nano-scale electronics, besides having great fault-tolerance ability. Research on the potential mapping of the HDM onto CMOL (hybrid CMOS/nanoelectronic circuits) nanogrids provides useful insight into the development of non-von Neumann neuromorphic architectures and semiconductor industry. In this dissertation, we investigated the implementations of AMs on different hardware platforms, including microprocessor based personal computer (PC), PC cluster, field programmable gate arrays (FPGA), CMOS, and CMOL nanogrids. We studied two types of neural associative memory models, with and without temporal information. In this research, we first decomposed the computational models into basic and common operations, such as matrix-vector inner-product and k-winners-take-all (k-WTA). We then analyzed the baseline performance/price ratio of implementing the AMs with a PC. We continued with a similar performance/price analysis of the implementations on more parallel hardware platforms, such as PC cluster and FPGA. However, the majority of the research emphasized on the implementations with all digital and mixed-signal full-custom CMOS and CMOL nanogrids. In this dissertation, we draw the conclusion that the mixed-signal CMOL nanogrids exhibit the best performance/price ratio over other hardware platforms. We also highlighted some of the trade-offs between dedicated and virtualized hardware circuits for the HDM models. A simple time-multiplexing scheme for the digital CMOS implementations can achieve comparable throughput as the mixed-signal CMOL nanogrids

Physically Equivalent Intelligent Systems for Reasoning Under Uncertainty at Nanoscale

Machines today lack the inherent ability to reason and make decisions, or operate in the presence of uncertainty. Machine-learning methods such as Bayesian Networks (BNs) are widely acknowledged for their ability to uncover relationships and generate causal models for complex interactions. However, their massive computational requirement, when implemented on conventional computers, hinders their usefulness in many critical problem areas e.g., genetic basis of diseases, macro finance, text classification, environment monitoring, etc. We propose a new non-von Neumann technology framework purposefully architected across all layers for solving these problems efficiently through physical equivalence, enabled by emerging nanotechnology. The architecture builds on a probabilistic information representation and multi-domain mixed-signal circuit style, and is tightly coupled to a nanoscale physical layer that spans magnetic and electrical domains. Based on bottom-up device-circuit-architecture simulations, we show up to four orders of magnitude performance improvement (using computational resolution of 0.1) vs. best-of-breed multi-core machines with 100 processors, for BNs with about a million variables. Smaller problem sizes of ~100 variables can be realized at 20 mW power consumption and very low area around a few tenths of a mm2. Our vision is to enable solving complex Bayesian problems in real time, as well as enable intelligence capabilities at a small scale everywhere, ushering in a new era of machine intelligence