Delay models and design guidelines for MCML gates with resistor or PMOS load

In this paper we present propagation delay models for MCML gates with resistor- or triode-PMOS-based output I–V conversion. The dependence of the parasitic capacitance of triode PMOS devices is accurately evaluated for the first time in the literature. The proposed models are able to accurately predict the propagation delay as a function of the bias current ISS in different design scenarios which require different tradeoffs between speed, area and power efficiency. The proposed models are validated against transistor level simulations referring to a 28 ​nm CMOS process showing a maximum percentage error lower than 6.5%. Based on these models, a comparative analysis is carried out and useful guidelines for the design of MCML gates are proposed

MOS CURRENT MODE LOGIC (MCML) ANALYSIS FOR QUIET DIGITAL CIRCUITRY AND CREATION OF A STANDARD CELL LIBRARY FOR REDUCING THE DEVELOPMENT TIME OF MIXED-SIGNAL CHIPS

Many modern digital systems use forms of CMOS logical implementation due to the straight forward design nature of CMOS logic and minimal device area since CMOS uses fewer transistors than other logic families. To achieve high-performance requirements in mixed-signal chip development and quiet, noiseless circuitry, this thesis provides an alternative toCMOSin the form of MOS Current Mode Logic (MCML). MCML dissipates constant current and does not produce noise during value changing in a circuit CMOS circuits do. CMOS logical networks switch during clock ticks and with every device switching, noise is created on the supply and ground to deal with the transitions. Creating a noiseless standard cell library with MCML allows use of circuitry that uses low voltage switching with 1.5V between logic levels in a quiet or mixed-signal environment as opposed to the full rail to rail swinging of CMOS logic. This allows cohesive implementation with analog circuitry on the same chip due to constant current and lower switching ranges not creating rail noise during digital switching. Standard cells allow for the Cadence tools to automatically generate circuits and Cadence serves as the development platform for the MCML standard cells. The theory surrounding MCML is examined along with current and future applications well-suited for MCML are researched and explored with the goal of highlighting valid candidate circuits for MCML. Inverters and NAND gates with varying current drives are developed to meet these specialized goals and are simulated to prove viability for quiet, mixed-signal applications. Analysis and results show that MCML is a superior implementation choice compared toCMOSfor high speed and mixed signal applications due to frequency independent power dissipation and lack of generated noise during operation. Noise results show rail current deviations of 50nA to 300nA during switching over an average operating current of 20µA to 80µA respectively. The multiple order of magnitude difference between noise and signal allow the MCML cells to dissipate constant power and thus perform with no noise added to a system. Additional simulated results of a 31-stage ring oscillator result in a frequency for MCML of 1.57GHz simulated versus the 150.35MHz that MOSIS tested on a fabricated 31-stage CMOS oscillator. The layouts designed for the standard cell library conform to existing On Semiconductor ami06 technology dimensions and allow for design of any logical function to be fabricated. The I/O signals of each cell operate at the same input and output voltage swings which allow seamless integration with each other for implementation in any logical configuration

Energy-Efficient Neural Network Architectures

Emerging systems for artificial intelligence (AI) are expected to rely on deep neural networks (DNNs) to achieve high accuracy for a broad variety of applications, including computer vision, robotics, and speech recognition. Due to the rapid growth of network size and depth, however, DNNs typically result in high computational costs and introduce considerable power and performance overheads. Dedicated chip architectures that implement DNNs with high energy efficiency are essential for adding intelligence to interactive edge devices, enabling them to complete increasingly sophisticated tasks by extending battery lie. They are also vital for improving performance in cloud servers that support demanding AI computations. This dissertation focuses on architectures and circuit technologies for designing energy-efficient neural network accelerators. First, a deep-learning processor is presented for achieving ultra-low power operation. Using a heterogeneous architecture that includes a low-power always-on front-end and a selectively-enabled high-performance back-end, the processor dynamically adjusts computational resources at runtime to support conditional execution in neural networks and meet performance targets with increased energy efficiency. Featuring a reconfigurable datapath and a memory architecture optimized for energy efficiency, the processor supports multilevel dynamic activation of neural network segments, performing object detection tasks with 5.3x lower energy consumption in comparison with a static execution baseline. Fabricated in 40nm CMOS, the processor test-chip dissipates 0.23mW at 5.3 fps. It demonstrates energy scalability up to 28.6 TOPS/W and can be configured to run a variety of workloads, including severely power-constrained ones such as always-on monitoring in mobile applications. To further improve the energy efficiency of the proposed heterogeneous architecture, a new charge-recovery logic family, called zero-short-circuit current (ZSCC) logic, is proposed to decrease the power consumption of the always-on front-end. By relying on dedicated circuit topologies and a four-phase clocking scheme, ZSCC operates with significantly reduced short-circuit currents, realizing order-of-magnitude power savings at relatively low clock frequencies (in the order of a few MHz). The efficiency and applicability of ZSCC is demonstrated through an ANSI S1.11 1/3 octave filter bank chip for binaural hearing aids with two microphones per ear. Fabricated in a 65nm CMOS process, this charge-recovery chip consumes 13.8µW with a 1.75MHz clock frequency, achieving 9.7x power reduction per input in comparison with a 40nm monophonic single-input chip that represents the published state of the art. The ability of ZSCC to further increase the energy efficiency of the heterogeneous neural network architecture is demonstrated through the design and evaluation of a ZSCC-based front-end. Simulation results show 17x power reduction compared with a conventional static CMOS implementation of the same architecture.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147614/1/hsiwu_1.pd

ANALYSIS OF MOS CURRENT MODE LOGIC (MCML) AND IMPLEMENTATION OF MCML STANDARD CELL LIBRARY FOR LOW-NOISE DIGITAL CIRCUIT DESIGN

MOS current mode logic (MCML) offers low noise digital circuits that reduce noise that can cripple analog components in mixed-signal integrated circuits, when compared to CMOS digital circuits. An MCML standard cell library was developed for the Cadence Virtuoso Integrated Circuit (IC) design software that gives IC designers the ability to design complex, low noise digital circuits for use in mixed-signal and noise sensitive systems at a high level of abstraction, allowing them to get superior products to market faster than competitors. The MCML standard cell library developed and presented here allows for fast development of mixed signal circuits by providing quiet digital building block gates that reduce the simultaneous switching noise (SSN) by an order of magnitude over conventional CMOS based designs [3]. This thesis project developed the following digital gates in MCML as a standard cell library for general-purpose low noise and very low noise applications: inverter, buffer, NAND, AND, NOR, OR, XOR, NXOR, 2:1 MUX, CMOS to MCML, MCML to CMOS, and double edge triggered flip-flop (DETFF)

34th Midwest Symposium on Circuits and Systems-Final Program

Organized by the Naval Postgraduate School Monterey California. Cosponsored by the IEEE Circuits and Systems Society. Symposium Organizing Committee: General Chairman-Sherif Michael, Technical Program-Roberto Cristi, Publications-Michael Soderstrand, Special Sessions- Charles W. Therrien, Publicity: Jeffrey Burl, Finance: Ralph Hippenstiel, and Local Arrangements: Barbara Cristi

Low-Power, High-Speed Transceivers for Network-on-Chip Communication

Networks on chips (NoCs) are becoming popular as they provide a solution for the interconnection problems on large integrated circuits (ICs). But even in a NoC, link-power can become unacceptably high and data rates are limited when conventional data transceivers are used. In this paper, we present a low-power, high-speed source-synchronous link transceiver which enables a factor 3.3 reduction in link power together with an 80% increase in data-rate. A low-swing capacitive pre-emphasis transmitter in combination with a double-tail sense-amplifier enable speeds in excess of 9 Gb/s over a 2 mm twisted differential interconnect, while consuming only 130 fJ/transition without the need for an additional supply. Multiple transceivers can be connected back-to-back to create a source-synchronous transceiver-chain with a wave-pipelined clock, operating with 6sigma offset reliability at 5 Gb/s

Neural-network dedicated processor for solving competitive assignment problems

A neural-network processor for solving first-order competitive assignment problems consists of a matrix of N x M processing units, each of which corresponds to the pairing of a first number of elements of (R sub i) with a second number of elements (C sub j), wherein limits of the first number are programmed in row control superneurons, and limits of the second number are programmed in column superneurons as MIN and MAX values. The cost (weight) W sub ij of the pairings is programmed separately into each PU. For each row and column of PU's, a dedicated constraint superneuron insures that the number of active neurons within the associated row or column fall within a specified range. Annealing is provided by gradually increasing the PU gain for each row and column or increasing positive feedback to each PU, the latter being effective to increase hysteresis of each PU or by combining both of these techniques

Power Optimized Transceivers for Future Switched Networks

Network equipment power consumption is under increased scrutiny. To understand and decompose transceiver power consumption, we have created a toolkit incorporating a library of transceiver circuits in 45-nm CMOS and MOS current mode logic (MCML) and characterize power consumption using representative network traffic traces with digital synthesis and SPICE tools. Our toolkit includes all the components required to construct a library of different transceivers: line coding, frame alignment, channel bonding, serialization and deserialization, clock–data recovery, and clock generation. For optical transceivers, we show that photonic components and front end drivers only consume a small fraction (<22%) of total serial transceiver power. This implies that major reductions in optical transceiver power can only be obtained by paying attention to the physical layer circuits such as clock recovery and serial–parallel conversions. We propose a burst-mode physical layer protocol suitable for optically switched links that retains the beneficial transmission characteristics of 8b/10b, but, even without power gating and voltage controlled oscillator power optimization, reduces the power consumption during idle periods by 29% compared with a conventional 8b/10b transceiver. We have made the toolkit available to the community at large in the hope of stimulating work in this field