148,358 research outputs found

    Impact of parameter variations on circuits and microarchitecture

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    Parameter variations, which are increasing along with advances in process technologies, affect both timing and power. Variability must be considered at both the circuit and microarchitectural design levels to keep pace with performance scaling and to keep power consumption within reasonable limits. This article presents an overview of the main sources of variability and surveys variation-tolerant circuit and microarchitectural approaches.Peer ReviewedPostprint (published version

    Yield-driven power-delay-optimal CMOS full-adder design complying with automotive product specifications of PVT variations and NBTI degradations

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    We present the detailed results of the application of mathematical optimization algorithms to transistor sizing in a full-adder cell design, to obtain the maximum expected fabrication yield. The approach takes into account all the fabrication process parameter variations specified in an industrial PDK, in addition to operating condition range and NBTI aging. The final design solutions present transistor sizing, which depart from intuitive transistor sizing criteria and show dramatic yield improvements, which have been verified by Monte Carlo SPICE analysis

    RRAM variability and its mitigation schemes

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    Emerging technologies such as RRAMs are attracting significant attention due to their tempting characteristics such as high scalability, CMOS compatibility and non-volatility to replace the current conventional memories. However, critical causes of hardware reliability failures, such as process variation due to their nano-scale structure have gained considerable importance for acceptable memory yields. Such vulnerabilities make it essential to investigate new robust design strategies at the circuit system level. In this paper we have analyzed the RRAM variability phenomenon, its impact and variation tolerant techniques at the circuit level. Finally a variation-monitoring circuit is presented that discerns the reliable memory cells affected by process variability.Peer ReviewedPostprint (author's final draft

    Variation Resilient Adaptive Controller for Subthreshold Circuits

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    Subthreshold logic is showing good promise as a viable ultra-low-power circuit design technique for power-limited applications. For this design technique to gain widespread adoption, one of the most pressing concerns is how to improve the robustness of subthreshold logic to process and temperature variations. We propose a variation resilient adaptive controller for subthreshold circuits with the following novel features: new sensor based on time-to-digital converter for capturing the variations accurately as digital signatures, and an all-digital DC-DC converter incorporating the sensor capable of generating an operating operating Vdd from 0V to 1.2V with a resolution of 18.75mV, suitable for subthreshold circuit operation. The benefits of the proposed controller is reflected with energy improvement of up to 55% compared to when no controller is employed. The detailed implementation and validation of the proposed controller is discussed

    Fuse: A technique to anticipate failures due to degradation in ALUs

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    This paper proposes the fuse, a technique to anticipate failures due to degradation in any ALU (arithmetic logic unit), and particularly in an adder. The fuse consists of a replica of the weakest transistor in the adder and the circuitry required to measure its degradation. By mimicking the behavior of the replicated transistor the fuse anticipates the failure short before the first failure in the adder appears, and hence, data corruption and program crashes can be avoided. Our results show that the fuse anticipates the failure in more than 99.9% of the cases after 96.6% of the lifetime, even for pessimistic random within-die variations.Peer ReviewedPostprint (published version

    Spin-Based Neuron Model with Domain Wall Magnets as Synapse

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    We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and domain wall magnets for compact, programmable synapses. The spin based neuron-synapse units operate locally at ultra low supply voltage of 30mV resulting in low computation power. CMOS based inter-neuron communication is employed to realize network-level functionality. We corroborate circuit operation with physics based models developed for the spin devices. Simulation results for character recognition as a benchmark application shows 95% lower power consumption as compared to 45nm CMOS design
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