The new era of cognitive computing brings forth the grand challenge of developing systems capable of processing massive amounts of noisy multisensory data. This type of intelligent computing poses a set of constraints, including real-time operation, low-power consumption and scalability, which require a radical departure from conventional system design. Brain-inspired architectures offer tremendous promise in this area. To this end, we developed TrueNorth, a 65 mW real-time neurosynaptic processor that implements a non-von Neumann, low-power, highly-parallel, scalable, and defect-tolerant architecture. With 4096 neurosynaptic cores, the TrueNorth chip contains 1 million digital neurons and 256 million synapses tightly interconnected by an event-driven routing infrastructure. The fully digital 5.4 billion transistor implementation leverages existing CMOS scaling trends, while ensuring one-to-one correspondence between hardware and software. With such aggressive design metrics and the TrueNorth architecture breaking path with prevailing architectures, it is clear that conventional computer-aided design (CAD) tools could not be used for the design. As a result, we developed a novel design methodology that includes mixed asynchronous-synchronous circuits and a complete tool flow for building an event-driven, low-power neurosynaptic chip. The TrueNorth chip is fully configurable in terms of connectivity and neural parameters to allow custom configurations for a wide range of cognitive and sensory perception applications. To reduce the system's communication energy, we have adapted existing application-agnostic very large-scale integration CAD placement tools for mapping logical neural networks to the physical neurosynaptic core locations on the TrueNorth chips. With that, we have successfully demonstrated the use of TrueNorth-based systems in multiple applications, including visual object recognition, with higher performance and orders of magnitude lower power consumption than the same algorithms run on von Neumann architectures. The TrueNorth chip and its tool flow serve as building blocks for future cognitive systems, and give designers an opportunity to develop novel brain-inspired architectures and systems based on the knowledge obtained from this paper.

We report a systematic study on the impact of process and statistical variability on SRAM design in a 14nm SOI FinFET technology node. A comprehensive statistical compact modelling strategy is developed for the early delivery of reliable PDK model, which enables TCAD-based transistor-cell co-design and path finding during the early phase of a technology node.

We present a novel power-aware yield enhancement design methodology and reconfiguration scheme for deep submicron SRAM designs. We show that with the continued trend of raising array supply to counter process variations, it is more effective to use a per-element selectable virtual power-supply scenario as opposed to single array supply with traditional redundancy schemes. The element can be a bank, a sub-array, or an independent row/column, and the element's virtual supply value is determined based on fail bitmaps. The technique can also be used in conjunction with traditional redundancy schemes to further improve the efficiency. The supply and redundancy assignments can be obtained by relying on memory reconfiguration algorithms. For this, we propose a greedy yet accurate algorithm that runs in O(nlogn) as opposed to average case O(n2) traditional algorithms. The methodology leads to significant power savings ranging from 20% to 50% for 65nm technology. We expect the savings to increase in future technologies as leakage powers dominate. To the best of our knowledge, this is the first time such a methodology is applied to SRAM designs.

Independent gate control in double-gate (DG) devices enhances circuit performance and robustness while substantially reducing leakage and chip area. In this paper, we describe circuit techniques which take advantage of the independent biasing properties of symmetrical and asymmetrical DG devices in design. DG circuits at the 25-nm node are analyzed via mixed-mode numerical simulations using Taurus MEDICI. In dynamic circuits, we give examples of conditional keepers, charge sharing prevention scheme, and static keepers. A conditional keeper can dynamically achieve the optimal strength ratio between keeper and evaluation devices by utilizing the front- and backchannel currents in DG devices. A charge sharing mitigation scheme utilizing the back-gate of a logic transistor is then described. Static data retention scheme in dynamic circuits is proposed. A case study for analog applications using a voltage controlled oscillator (VCO) illustrates the specific advantages of DG devices.

We propose a virtual supply rail control technique that reduces SRAM leakage. This method encompasses a cell-based image, serial tiling, pitch matching, small drive device overhead, and controlled power-on currents while incurring small circuit overhead. A virtual rail cell contains both the sleep transistor fingers and input/output drive transistors. The usual overhead associated with the drive circuit that controls the sleep transistors is significantly reduced due to reduced wire load and improved drive efficiency. This technique provides gradual power-on characteristics and good signal slews while effectively mitigating leakage current, maintaining read/write speed and achieving power-on latency compatible with high-performance designs.

This paper presents a detailed study on the impact of floating body in partially-depleted (PD) SOI MOSFET on various digital VLSI CMOS circuit families. The parasitic bipolar effect resulting from the floating body is shown to degrade the circuit noise margin and stability in general. In certain dynamic circuits and wide multiplexers, the parasitic bipolar effect is shown to cause logic state error if not properly accounted for.