Optimal digital system design in deep submicron technology

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 165-174).The optimization of a digital system in deep submicron technology should be done with two basic principles: energy waste reduction and energy-delay tradeoff. Increased energy resources obtained through energy waste reduction are utilized through energy-delay tradeoffs. The previous practice of obliviously pursuing performance has led to the rapid increase in energy consumption. While energy waste due to unnecessary switching could be reduced with small increases in logic complexity, leakage energy waste still remains as a major design challenge. We find that fine-grain dynamic leakage reduction (FG-DLR), turning off small subblocks for short idle intervals, is the key for successful leakage energy saving. We introduce an FG-DLR circuit technique, Leakage Biasing, which uses leakage currents themselves to bias the circuit into the minimum leakage state, and apply it to primary SRAM arrays for bitline leakage reduction (Leakage-Biased Bitlines) and to domino logic (Leakage-Biased Domino). We also introduce another FG-DLR circuit technique, Dynamic Resizing, which dynamically downsizes transistors on idle paths while maintaining the performance along active critical paths, and apply it to static CMOS circuits.(cont.) We show that significant energy reduction can be achieved at the same computation throughput and communication bandwidth by pipelining logic gates and wires. We find that energy saved by pipelining datapaths is eventually limited by latch energy overhead, leading to a power-optimal pipelining. Structuring global wires into on-chip networks provides a better environment for pipelining and leakage energy saving. We show that the energy-efficiency increase through replacement with dynamically packet-routed networks is bounded by router energy overhead. Finally, we provide a way of relaxing the peak power constraint. We evaluate the use of Activity Migration (AM) for hot spot removal. AM spreads heat by transporting computation to a different location on the die. We show that AM can be used either to increase the power that can be dissipated by a given package, or to lower the operating temperature and hence the operating energy.by Seongmoo Heo.Ph.D

Microarchitectural Low-Power Design Techniques for Embedded Microprocessors

With the omnipresence of embedded processing in all forms of electronics today, there is a strong trend towards wireless, battery-powered, portable embedded systems which have to operate under stringent energy constraints. Consequently, low power consumption and high energy efficiency have emerged as the two key criteria for embedded microprocessor design. In this thesis we present a range of microarchitectural low-power design techniques which enable the increase of performance for embedded microprocessors and/or the reduction of energy consumption, e.g., through voltage scaling. In the context of cryptographic applications, we explore the effectiveness of instruction set extensions (ISEs) for a range of different cryptographic hash functions (SHA-3 candidates) on a 16-bit microcontroller architecture (PIC24). Specifically, we demonstrate the effectiveness of light-weight ISEs based on lookup table integration and microcoded instructions using finite state machines for operand and address generation. On-node processing in autonomous wireless sensor node devices requires deeply embedded cores with extremely low power consumption. To address this need, we present TamaRISC, a custom-designed ISA with a corresponding ultra-low-power microarchitecture implementation. The TamaRISC architecture is employed in conjunction with an ISE and standard cell memories to design a sub-threshold capable processor system targeted at compressed sensing applications. We furthermore employ TamaRISC in a hybrid SIMD/MIMD multi-core architecture targeted at moderate to high processing requirements (> 1 MOPS). A range of different microarchitectural techniques for efficient memory organization are presented. Specifically, we introduce a configurable data memory mapping technique for private and shared access, as well as instruction broadcast together with synchronized code execution based on checkpointing. We then study an inherent suboptimality due to the worst-case design principle in synchronous circuits, and introduce the concept of dynamic timing margins. We show that dynamic timing margins exist in microprocessor circuits, and that these margins are to a large extent state-dependent and that they are correlated to the sequences of instruction types which are executed within the processor pipeline. To perform this analysis we propose a circuit/processor characterization flow and tool called dynamic timing analysis. Moreover, this flow is employed in order to devise a high-level instruction set simulation environment for impact-evaluation of timing errors on application performance. The presented approach improves the state of the art significantly in terms of simulation accuracy through the use of statistical fault injection. The dynamic timing margins in microprocessors are then systematically exploited for throughput improvements or energy reductions via our proposed instruction-based dynamic clock adjustment (DCA) technique. To this end, we introduce a 6-stage 32-bit microprocessor with cycle-by-cycle DCA. Besides a comprehensive design flow and simulation environment for evaluation of the DCA approach, we additionally present a silicon prototype of a DCA-enabled OpenRISC microarchitecture fabricated in 28 nm FD-SOI CMOS. The test chip includes a suitable clock generation unit which allows for cycle-by-cycle DCA over a wide range with fine granularity at frequencies exceeding 1 GHz. Measurement results of speedups and power reductions are provided

Circuits and Systems Advances in Near Threshold Computing

Modern society is witnessing a sea change in ubiquitous computing, in which people have embraced computing systems as an indispensable part of day-to-day existence. Computation, storage, and communication abilities of smartphones, for example, have undergone monumental changes over the past decade. However, global emphasis on creating and sustaining green environments is leading to a rapid and ongoing proliferation of edge computing systems and applications. As a broad spectrum of healthcare, home, and transport applications shift to the edge of the network, near-threshold computing (NTC) is emerging as one of the promising low-power computing platforms. An NTC device sets its supply voltage close to its threshold voltage, dramatically reducing the energy consumption. Despite showing substantial promise in terms of energy efficiency, NTC is yet to see widescale commercial adoption. This is because circuits and systems operating with NTC suffer from several problems, including increased sensitivity to process variation, reliability problems, performance degradation, and security vulnerabilities, to name a few. To realize its potential, we need designs, techniques, and solutions to overcome these challenges associated with NTC circuits and systems. The readers of this book will be able to familiarize themselves with recent advances in electronics systems, focusing on near-threshold computing

Low-voltage embedded biomedical processor design

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 180-190).Advances in mobile electronics are fueling new possibilities in a variety of applications, one of which is ambulatory medical monitoring with body-worn or implanted sensors. Digital processors on such sensors serve to analyze signals in real-time and extract key features for transmission or storage. To support diverse and evolving applications, the processor should be flexible, and to extend sensor operating lifetime, the processor should be energy-efficient. This thesis focuses on architectures and circuits for low power biomedical signal processing. A general-purpose processor is extended with custom hardware accelerators to reduce the cycle count and energy for common tasks, including FIR and median filtering as well as computing FFTs and mathematical functions. Improvements to classic architectures are proposed to reduce power and improve versatility: an FFT accelerator demonstrates a new control scheme to reduce datapath switching activity, and a modified CORDIC engine features increased input range and decreased quantization error over conventional designs. At the system level, the addition of accelerators increases leakage power and bus loading; strategies to mitigate these costs are analyzed in this thesis. A key strategy for improving energy efficiency is to aggressively scale the power supply voltage according to application performance demands. However, increased sensitivity to variation at low voltages must be mitigated in logic and SRAM design. For logic circuits, a design flow and a hold time verification methodology addressing local variation are proposed and demonstrated in a 65nm microcontroller functioning at 0.3V. For SRAMs, a model for the weak-cell read current is presented for near-V supply voltages, and a self-timed scheme for reducing internal bus glitches is employed with low leakage overhead. The above techniques are demonstrated in a 0.5-1.OV biomedical signal processing platform in 0.13p-Lm CMOS. The use of accelerators for key signal processing enabled greater than 10x energy reduction in two complete EEG and EKG analysis applications, as compared to implementations on a conventional processor.by Joyce Y. S. Kwong.Ph.D

Energy-aware synthesis for networks on chip architectures

The Network on Chip (NoC) paradigm was introduced as a scalable communication infrastructure for future System-on-Chip applications. Designing application specific customized communication architectures is critical for obtaining low power, high performance solutions. Two significant design automation problems are the creation of an optimized configuration, given application requirement the implementation of this on-chip network. Automating the design of on-chip networks requires models for estimating area and energy, algorithms to effectively explore the design space and network component libraries and tools to generate the hardware description. Chip architects are faced with managing a wide range of customization options for individual components, routers and topology. As energy is of paramount importance, the effectiveness of any custom NoC generation approach lies in the availability of good energy models to effectively explore the design space. This thesis describes a complete NoC synthesis flow, called NoCGEN, for creating energy-efficient custom NoC architectures. Three major automation problems are addressed: custom topology generation, energy modeling and generation. An iterative algorithm is proposed to generate application specific point-to-point and packet-switched networks. The algorithm explores the design space for efficient topologies using characterized models and a system-level floorplanner for evaluating placement and wire-energy. Prior to our contribution, building an energy model required careful analysis of transistor or gate implementations. To alleviate the burden, an automated linear regression-based methodology is proposed to rapidly extract energy models for many router designs. The resulting models are cycle accurate with low-complexity and found to be within 10% of gate-level energy simulations, and execute several orders of magnitude faster than gate-level simulations. A hardware description of the custom topology is generated using a parameterizable library and custom HDL generator. Fully reusable and scalable network components (switches, crossbars, arbiters, routing algorithms) are described using a template approach and are used to compose arbitrary topologies. A methodology for building and composing routers and topologies using a template engine is described. The entire flow is implemented as several demonstrable extensible tools with powerful visualization functionality. Several experiments are performed to demonstrate the design space exploration capabilities and compare it against a competing min-cut topology generation algorithm

Design Methodologies and CAD Tools for Leakage Power Optimization in FPGAs

The scaling of the CMOS technology has precipitated an exponential increase in both subthreshold and gate leakage currents in modern VLSI designs. Consequently, the contribution of leakage power to the total chip power dissipation for CMOS designs is increasing rapidly, which is estimated to be 40% for the current technology generations and is expected to exceed 50% by the 65nm CMOS technology. In FPGAs, the power dissipation problem is further aggravated when compared to ASIC designs because FPGA use more transistors per logic function when compared to ASIC designs. Consequently, solving the leakage power problem is pivotal to devising power-aware FPGAs in the nanometer regime. This thesis focuses on devising both architectural and CAD techniques for leakage mitigation in FPGAs. Several CAD and architectural modifications are proposed to reduce the impact of leakage power dissipation on modern FPGAs. Firstly, multi-threshold CMOS (MTCMOS) techniques are introduced to FPGAs to permanently turn OFF the unused resources of the FPGA, FPGAs are characterized with low utilization percentages that can reach 60%. Moreover, such architecture enables the dynamic shutting down of the FPGA idle parts, thus reducing the standby leakage significantly. Employing the MTCMOS technique in FPGAs requires several changes to the FPGA architecture, including the placement and routing of the sleep signals and the MTCMOS granularity. On the CAD level, the packing and placement stages are modified to allow the possibility of dynamically turning OFF the idle parts of the FPGA. A new activity generation algorithm is proposed and implemented that aims to identify the logic blocks in a design that exhibit similar idleness periods. Several criteria for the activity generation algorithm are used, including connectivity and logic function. Several versions of the activity generation algorithm are implemented to trade power savings with runtime. A newly developed packing algorithm uses the resulting activities to minimize leakage power dissipation by packing the logic blocks with similar or close activities together. By proposing an FPGA architecture that supports MTCMOS and developing a CAD tool that supports the new architecture, an average power savings of 30% is achieved for a 90nm CMOS process while incurring a speed penalty of less than 5%. This technique is further extended to provide a timing-sensitive version of the CAD flow to vary the speed penalty according to the criticality of each logic block. Secondly, a new technique for leakage power reduction in FPGAs based on the use of input dependency is developed. Both subthreshold and gate leakage power are heavily dependent on the input state. In FPGAs, the effect of input dependency is exacerbated due to the use of pass-transistor multiplexer logic, which can exhibit up to 50% variation in leakage power due to the input states. In this thesis, a new algorithm is proposed that uses bit permutation to reduce subthreshold and gate leakage power dissipation in FPGAs. The bit permutation algorithm provides an average leakage power reduction of 40% while having less than 2% impact on the performance and no penalty on the design area. Thirdly, an accurate probabilistic power model for FPGAs is developed to quantify the savings from the proposed leakage power reduction techniques. The proposed power model accounts for dynamic, short circuit, and leakage power (including both subthreshold and gate leakage power) dissipation in FPGAs. Moreover, the power model accounts for power due to glitches, which accounts for almost 20% of the dynamic power dissipation in FPGAs. The use of probabilities in the power model makes it more computationally efficient than the other FPGA power models in the literature that rely on long input sequence simulations. One of the main advantages of the proposed power model is the incorporation of spatial correlation while estimating the signal probability. Other probabilistic FPGA power models assume spatial independence among the design signals, thus overestimating the power calculations. In the proposed model, a probabilistic model is proposed for spatial correlations among the design signals. Moreover, a different variation is proposed that manages to capture most of the spatial correlations with minimum impact on runtime. Furthermore, the proposed power model accounts for the input dependency of subthreshold and gate leakage power dissipation. By comparing the proposed power model to HSpice simulation, the estimated power is within 8% and is closer to HSpice simulations than other probabilistic FPGA power models by an average of 20%

Energy Efficient Design for Deep Sub-micron CMOS VLSIs

Over the past decade, low power, energy efficient VLSI design has been the focal point of active research and development. The rapid technology scaling, the growing integration capacity, and the mounting active and leakage power dissipation are contributing to the growing complexity of modern VLSI design. Careful power planning on all design levels is required. This dissertation tackles the low-power, low-energy challenges in deep sub-micron technologies on the architecture and circuit levels. Voltage scaling is one of the most efficient ways for reducing power and energy. For ultra-low voltage operation, a new circuit technique which allows bulk CMOS circuits to work in the sub-0. 5V supply territory is presented. The threshold voltage of the slow PMOS transistor is controlled dynamically to get a lower threshold voltage during the active mode. Due to the reduced threshold voltage, switching speed becomes faster while active leakage current is increased. A technique to dynamically manage active leakage current is presented. Energy reduction resulting from using the proposed structure is demonstrated through simulations of different circuits with different levels of complexity. As technology scales, the mounting leakage current and degraded noise immunity impact performance especially that of high performance dynamic circuits. Dual threshold technology shows a good potential for leakage reduction while meeting performance goals. A model for optimally selecting threshold voltages and transistor sizes in wide fan-in dynamic circuits is presented. On the circuit level, a novel circuit level technique which handles the trade-off between noise immunity and energy dissipation for wide fan-in dynamic circuits is presented. Energy efficiency of the proposed wide fan-in dynamic circuit is further enhanced through efficient low voltage operation. Another direct consequence of technology scaling is the growing impact of interconnect parasitics and process variations on performance. Traditionally, worst case process, parasitics, and environmental conditions are considered. Designing for worst case guarantees a fail-safe operation but requires a large delay and voltage margins. This large margin can be recovered if the design can adapt to the actual silicon conditions. Dynamic voltage scaling is considered a key enabler in reducing such margin. An on-chip process identifier to recover the margin required due to process variations is described. The proposed architecture adjusts supply voltage using a hybrid between the one-time voltage setting and the continuous monitoring modes of operation. The interconnect impact on delay is minimized through a novel adaptive voltage scaling architecture. The proposed system recovers the large delay and voltage margins required by conventional systems by closely tracking the actual critical path at anytime. By tracking the actual critical path, the proposed system is robust and more energy efficient compared to both the conventional open-loop and closed-loop systems

A Structured Design Methodology for High Performance VLSI Arrays

abstract: The geometric growth in the integrated circuit technology due to transistor scaling also with system-on-chip design strategy, the complexity of the integrated circuit has increased manifold. Short time to market with high reliability and performance is one of the most competitive challenges. Both custom and ASIC design methodologies have evolved over the time to cope with this but the high manual labor in custom and statistic design in ASIC are still causes of concern. This work proposes a new circuit design strategy that focuses mostly on arrayed structures like TLB, RF, Cache, IPCAM etc. that reduces the manual effort to a great extent and also makes the design regular, repetitive still achieving high performance. The method proposes making the complete design custom schematic but using the standard cells. This requires adding some custom cells to the already exhaustive library to optimize the design for performance. Once schematic is finalized, the designer places these standard cells in a spreadsheet, placing closely the cells in the critical paths. A Perl script then generates Cadence Encounter compatible placement file. The design is then routed in Encounter. Since designer is the best judge of the circuit architecture, placement by the designer will allow achieve most optimal design. Several designs like IPCAM, issue logic, TLB, RF and Cache designs were carried out and the performance were compared against the fully custom and ASIC flow. The TLB, RF and Cache were the part of the HEMES microprocessor.Dissertation/ThesisPh.D. Electrical Engineering 201

Low power digital baseband core for wireless Micro-Neural-Interface using CMOS sub/near-threshold circuit

This thesis presents the work on designing and implementing a low power digital baseband core with custom-tailored protocol for wirelessly powered Micro-Neural-Interface (MNI) System-on-Chip (SoC) to be implanted within the skull to record cortical neural activities. The core, on the tag end of distributed sensors, is designed to control the operation of individual MNI and communicate and control MNI devices implanted across the brain using received downlink commands from external base station and store/dump targeted neural data uplink in an energy efficient manner. The application specific protocol defines three modes (Time Stamp Mode, Streaming Mode and Snippet Mode) to extract neural signals with on-chip signal conditioning and discrimination. In Time Stamp Mode, Streaming Mode and Snippet Mode, the core executes basic on-chip spike discrimination and compression, real-time monitoring and segment capturing of neural signals so single spike timing as well as inter-spike timing can be retrieved with high temporal and spatial resolution. To implement the core control logic using sub/near-threshold logic, a novel digital design methodology is proposed which considers INWE (Inverse-Narrow-Width-Effect), RSCE (Reverse-Short-Channel-Effect) and variation comprehensively to size the transistor width and length accordingly to achieve close-to-optimum digital circuits. Ultra-low-power cell library containing 67 cells including physical cells and decoupling capacitor cells using the optimum fingers is designed, laid-out, characterized, and abstracted. A robust on-chip sense-amp-less SRAM memory (8X32 size) for storing neural data is implemented using 8T topology and LVT fingers. The design is validated with silicon tapeout and measurement shows the digital baseband core works at 400mV and 1.28 MHz system clock with an average power consumption of 2.2 μW, resulting in highest reported communication power efficiency of 290Kbps/μW to date

Power Management for Deep Submicron Microprocessors

As VLSI technology scales, the enhanced performance of smaller transistors comes at the expense of increased power consumption. In addition to the dynamic power consumed by the circuits there is a tremendous increase in the leakage power consumption which is further exacerbated by the increasing operating temperatures. The total power consumption of modern processors is distributed between the processor core, memory and interconnects. In this research two novel power management techniques are presented targeting the functional units and the global interconnects. First, since most leakage control schemes for processor functional units are based on circuit level techniques, such schemes inherently lack information about the operational profile of higher-level components of the system. This is a barrier to the pivotal task of predicting standby time. Without this prediction, it is extremely difficult to assess the value of any leakage control scheme. Consequently, a methodology that can predict the standby time is highly beneficial in bridging the gap between the information available at the application level and the circuit implementations. In this work, a novel Dynamic Sleep Signal Generator (DSSG) is presented. It utilizes the usage traces extracted from cycle accurate simulations of benchmark programs to predict the long standby periods associated with the various functional units. The DSSG bases its decisions on the current and previous standby state of the functional units to accurately predict the length of the next standby period. The DSSG presents an alternative to Static Sleep Signal Generation (SSSG) based on static counters that trigger the generation of the sleep signal when the functional units idle for a prespecified number of cycles. The test results of the DSSG are obtained by the use of a modified RISC superscalar processor, implemented by SimpleScalar, the most widely accepted open source vehicle for architectural analysis. In addition, the results are further verified by a Simultaneous Multithreading simulator implemented by SMTSIM. Leakage saving results shows an increase of up to 146% in leakage savings using the DSSG versus the SSSG, with an accuracy of 60-80% for predicting long standby periods. Second, chip designers in their effort to achieve timing closure, have focused on achieving the lowest possible interconnect delay through buffer insertion and routing techniques. This approach, though, taxes the power budget of modern ICs, especially those intended for wireless applications. Also, in order to achieve more functionality, die sizes are constantly increasing. This trend is leading to an increase in the average global interconnect length which, in turn, requires more buffers to achieve timing closure. Unconstrained buffering is bound to adversely affect the overall chip performance, if the power consumption is added as a major performance metric. In fact, the number of global interconnect buffers is expected to reach hundreds of thousands to achieve an appropriate timing closure. To mitigate the impact of the power consumed by the interconnect buffers, a power-efficient multi-pin routing technique is proposed in this research. The problem is based on a graph representation of the routing possibilities, including buffer insertion and identifying the least power path between the interconnect source and set of sinks. The novel multi-pin routing technique is tested by applying it to the ISPD and IBM benchmarks to verify the accuracy, complexity, and solution quality. Results obtained indicate that an average power savings as high as 32% for the 130-nm technology is achieved with no impact on the maximum chip frequency