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.

TrueNorth: Design and Tool Flow of a 65 mW 1 Million Neuron Programmable Neurosynaptic Chip

An experimental technique is described for observing the effects of switching transients in digital MOS circuits that perturb analog circuits integrated on the same die by means of coupling through the substrate Various approaches to reducing substrate crosstalk (the use of physical separation of analog and digital circuits, guard rings, and a low-inductance substrate bias) are evaluated experimentally for a CMOS technology with a substrate comprising an epitaxial layer grown on a heavily doped bulk wafer Observations indicate that reducing the inductance in the substrate bias is the most effective Device simulations are used to show how crosstalk propagates via the heavily doped bulk and to predict the nature of substrate crosstalk in CMOS technologies integrated in uniform, lightly doped bulk substrates, showing that in such cases the substrate noise is highly dependent on layout geometry A method of including substrate effects in SPICE simulations for circuits fabricated on epitaxial, heavily doped substrates is developed >

Experimental Results and Modeling Techniques for Substrate Noise in Mixed-Signal Integrated Circuits

Context-aware systems and methods, location-aware systems and methods, context-aware vehicles and methods of operating the same, and location-aware vehicles and methods of operating the same are described. In various embodiments, a context or location service module, implemented in software, determines a vehicle context or a vehicle location based upon information that it receives from various context providers or location providers respectively. Software executing on a vehicle's computer can then cause one or more applications that are associated with a vehicle computer to be modified in a manner that changes their behavior. The behavior modification is based on the current context or location of the vehicle and thus provides a context-specific or location-specific user experience. The context or location can be ascertained through the use of one or more hierarchical tree structures that comprises individual nodes. Each node is associated with a context or location. The nodes are traversable by the vehicle's software to ascertain a more complete context or location.

Context-aware systems and methods, location-aware systems and methods, context-aware vehicles and methods of operating the same, and location-aware vehicles and methods of operating the same

In this paper, we propose a framework for low-energy digital signal processing (DSP), where the supply voltage is scaled beyond the critical voltage imposed by the requirement to match the critical path delay to the throughput. This deliberate introduction of input-dependent errors leads to degradation in the algorithmic performance, which is compensated for via algorithmic noise-tolerance (ANT) schemes. The resulting setup that comprises of the DSP architecture operating at subcritical voltage and the error control scheme is referred to as soft DSP. The effectiveness of the proposed scheme is enhanced when arithmetic units with a higher "delay imbalance" are employed. A prediction-based error-control scheme is proposed to enhance the performance of the filtering algorithm in the presence of errors due to soft computations. For a frequency selective filter, it is shown that the proposed scheme provides 60-81% reduction in energy dissipation for filter bandwidths up to 0.5 /spl pi/ (where 2 /spl pi/ corresponds to the sampling frequency f/sub s/) over that achieved via conventional architecture and voltage scaling, with a maximum of 0.5-dB degradation in the output signal-to-noise ratio (SNR/sub o/). It is also shown that the proposed algorithmic noise-tolerance schemes can also be used to improve the performance of DSP algorithms in presence of bit-error rates of up to 10/sup -3/ due to deep submicron (DSM) noise.

Soft digital signal processing

This paper reviews the design challenges that current and future processors must face, with stringent power limits, high-frequency targets, and the continuing system integration trends. This paper then describes the architecture, circuit design, and physical implementation of a first-generation Cell processor and the design techniques used to overcome the above challenges. A Cell processor consists of a 64-bit Power Architecture processor coupled with multiple synergistic processors, a flexible IO interface, and a memory interface controller that supports multiple operating systems including Linux. This multi-core SoC, implemented in 90-nm SOI technology, achieved a high clock rate by maximizing custom circuit design while maintaining reasonable complexity through design modularity and reuse.

Overview of the architecture, circuit design, and physical implementation of a first-generation cell processor

As technology scales into the deep submicron regime, noise immunity is becoming a metric of comparable importance to area, timing, and power for the analysis and design of VLSI systems. This paper defines noise as it pertains to digital systems and addresses the technology trends which are bringing noise issues to the forefront. The noise sources which are plaguing digital systems are explained. A metric referred to as noise stability is defined, and a static noise analysis methodology based on this metric is introduced to demonstrate how noise can be analyzed systematically. Analysis issues associated with on-chip interconnect are also considered. This paper concludes with a discussion of the device, circuit, layout, and logic design issues associated with noise.

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Noise in deep submicron digital design

As technology scales into the deep submicron regime, noise immunity is becoming a metric of comparable importance to area, timing, and power for the analysis and design of very large scale integrated (VLSI) systems. A metric for noise immunity is defined, and a static noise analysis methodology based on this noise-stability metric is introduced to demonstrate how noise can be analyzed systematically on a full-chip basis using simulation-based transistor-level analysis. We then describe Harmony, a two-level (macro and global) hierarchical implementation of static noise analysis. At the macro level, simplified interconnect models and timing assumptions guide efficient analysis. The global level involves a careful combination of static noise analysis, static timing analysis, and detailed interconnect macromodels based on reduced-order modeling techniques. We describe how the interconnect macromodels are practically employed to perform coupling analysis and how timing constraints can be used to limit pessimism in the analysis.

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Harmony: static noise analysis of deep submicron digital integrated circuits

Noise is becoming one of the most important metrics in the design of VLSI systems, certainly of comparable importance to area, timing, and power. In this paper, we describe Global Harmony, a methodology for the analysis of coupling noise in the global interconnect of large VLSI chips, being developed for the design of high-performance microprocessors. The architecture of Global Harmony involves a careful combination of static noise analysis, static timing analysis, and reduced-order modelling techniques. We describe a reduced-order modelling approach that allows for passive multiport reduction of RC netlists as impedance macromodels while preserving the symmetry and sparsity of the state matrices for efficient storage. We describe how the macromodels are practically employed to perform coupling analysis and how timing constraints can be used to limit pessimism in the analysis.

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Global harmony: coupled noise analysis for full-chip RC interconnect networks

As feature sizes decrease and clock frequencies increase, noise is becoming a greater concern in digital IC design. The authors describe a verification metric, noise stability, which guarantees functionality in the presence of noise, and a CAD technique, static noise analysis, for applying this metric on a chipwide basis.

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Conquering noise in deep-submicron digital ICs

A computer-based method automatically synthesizes, optimizes and compiles high performance control logic using SRCMOS LOGIC ARRAY MACROS, abbreviated as SLAMs. The method includes a series of steps that transform a high level design description into a set of SLAMs, and includes the steps of partitioning the logic description of a unit into blocks that are suitable for mapping to a target SLAM structure; mapping each logic partition to the target SLAM structure; creating a configuration and relative layout for the internal structure for each SLAM; creating an external description for each SLAM, each description being of sufficient detail to carry out physical design and integration of the unit which contains the SLAM; assembling the partitions implemented as SLAMs with other macros in the unit; resolving interface conflicts between the different macros by selecting appropriate signal interfaces for various SLAMs; repeatedly changing the external specifications of the various SLAMs; analyzing the performance of the unit; automatically compiling the schematic and layout of each SLAM within the unit based on the configuration and relative layout; and assembling the macros and analyzing the design for design rule violations.

Vinod Narayanan

Papers

Noise in deep submicron digital design

Harmony: static noise analysis of deep submicron digital integrated circuits

Global harmony: coupled noise analysis for full-chip RC interconnect networks

Conquering noise in deep-submicron digital ICs

Method and apparatus for synthesizing and optimizing control logic based on SRCMOS logic array macros