IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 

About: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems is an academic journal published by Institute of Electrical and Electronics Engineers. The journal publishes majorly in the area(s): Computer science & Routing (electronic design automation). It has an ISSN identifier of 0278-0070. Over the lifetime, 6781 publications have been published receiving 241645 citations. The journal is also known as: Transactions on computer-aided design of integrated circuits and systems & IEEE transactions on computer aided design of integrated circuits and systems.

Asymptotic waveform evaluation for timing analysis

Abstract: Asymptotic waveform evaluation (AWE) provides a generalized approach to linear RLC circuit response approximations. The RLC interconnect model may contain floating capacitors, grounded resistors, inductors, and even linear controlled sources. The transient portion of the response is approximated by matching the initial boundary conditions and the first 2q-1 moments of the exact response to a lower-order q-pole model. For the case of an RC tree model, a first-order AWE approximation reduces to the RC tree methods. >

PRIMA: passive reduced-order interconnect macromodeling algorithm

Abstract: This paper describes an algorithm for generating provably passive reduced-order N-port models for RLC interconnect circuits. It is demonstrated that, in addition to macromodel stability, macromodel passivity is needed to guarantee the overall circuit stability once the active and passive driver/load models are connected. The approach proposed here, PRIMA, is a general method for obtaining passive reduced-order macromodels for linear RLC systems. In this paper, PRIMA is demonstrated in terms of a simple implementation which extends the block Arnoldi technique to include guaranteed passivity while providing superior accuracy. While the same passivity extension is not possible for MPVL, comparable accuracy in the frequency domain for all examples is observed.

Efficient linear circuit analysis by Pade approximation via the Lanczos process

Abstract: In this paper, we introduce PVL, an algorithm for computing the Pade approximation of Laplace-domain transfer functions of large linear networks via a Lanczos process. The PVL algorithm has significantly superior numerical stability, while retaining the same efficiency as algorithms that compute the Pade approximation directly through moment matching, such as AWE and its derivatives. As a consequence, it produces more accurate and higher-order approximations, and it renders unnecessary many of the heuristics that AWE and its derivatives had to employ. The algorithm also computes an error bound that permits to identify the true poles and zeros of the original network. We present results of numerical experiments with the PVL algorithm for several large examples. >

MIS: A Multiple-Level Logic Optimization System

Abstract: MIS is both an interactive and a batch-oriented multilevel logic synthesis and minimization system. MIS starts from the combinational logic extracted, typically, from a high-level description of a macrocell. It produces a multilevel set of optimized logic equations preserving the input-output behavior. The system includes both fast and slower (but more optimal) versions of algorithms for minimizing the area, and global timing optimization algorithms to meet system-level timing constraints. This paper provides an overview of the system and a description of the algorithms used. Included are some examples illustrating an input language used for specifying logic and don't-cares. Parts on an industrial chip have been re-synthesized using MIS with favorable results as compared to equivalent manual designs.

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

Abstract: 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.