An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.Comment: To appear at the DSN 2020 conferenc

Diseño de circuitos analógicos y de señal mixta con consideraciones de diseño físico y variabilidad

Advances in microelectronic technology has been based on an increasing capacity to integrate transistors, moving this industry to the nanoelectronics realm in recent years. Moore’s Law [1] has predicted (and somehow governed) the growth of the capacity to integrate transistors in a single IC. Nevertheless, while this capacity has grown steadily, the increasing number of design tasks that are involved in the creation of the integrated circuit and their complexity has led to a phenomenon known as the ``design gap´´. This is the difference between what can theoretically be integrated and what can practically be designed. Since the early 2000s, the International Technology Roadmap of Semiconductors (ITRS) reports, published by the Semiconductor Industry Association (SIA), alert about the necessity to limit the growth of the design cost by increasing the productivity of the designer to continue the semiconductor industry’s growth. Design automation arises as a key element to close this ”design gap”. In this sense, electronic design automation (EDA) tools have reached a level of maturity for digital circuits that is far behind the EDA tools that are made for analog circuit design automation. While digital circuits rely, in general, on two stable operation states (which brings inherent robustness against numerous imperfections and interferences, leading to few design constraints like area, speed or power consumption), analog signal processing, on the other hand, demands compliance with lots of constraints (e.g., matching, noise, robustness, ...). The triumph of digital CMOS circuits, thanks to their mentioned robustness, has, ultimately, facilitated the way that circuits can be processed by algorithms, abstraction levels and description languages, as well as how the design information traverse the hierarchical levels of a digital system. The field of analog design automation faces many more difficulties due to the many sources of perturbation, such as the well-know process variability, and the difficulty in treating these systematically, like digital tools can do. In this Thesis, different design flows are proposed, focusing on new design methodologies for analog circuits, thus, trying to close the ”gap” between digital and analog EDA tools. In this chapter, the most important sources for perturbations and their impact on the analog design process are discussed in Section 1.2. The traditional analog design flow is discussed in 1.3. Emerging design methodologies that try to reduce the ”design gap” are presented in Section 1.4 where the key concept of Pareto-Optimal Front (POF) is explained. This concept, brought from the field of economics, models the analog circuit performances into a set of solutions that show the optimal trade-offs among conflicting circuit performances (e.g. DC-gain and unity-gain frequency). Finally, the goals of this thesis are presented in Section 1.5

Statistical Power Supply Dynamic Noise Prediction in Hierarchical Power Grid and Package Networks

One of the most crucial high performance systems-on-chip design challenge is to front their power supply noise sufferance due to high frequencies, huge number of functional blocks and technology scaling down. Marking a difference from traditional post physical-design static voltage drop analysis, /a priori dynamic voltage drop/evaluation is the focus of this work. It takes into account transient currents and on-chip and package /RLC/ parasitics while exploring the power grid design solution space: Design countermeasures can be thus early defined and long post physical-design verification cycles can be shortened. As shown by an extensive set of results, a carefully extracted and modular grid library assures realistic evaluation of parasitics impact on noise and facilitates the power network construction; furthermore statistical analysis guarantees a correct current envelope evaluation and Spice simulations endorse reliable result

Product assurance technology for custom LSI/VLSI electronics

The technology for obtaining custom integrated circuits from CMOS-bulk silicon foundries using a universal set of layout rules is presented. The technical efforts were guided by the requirement to develop a 3 micron CMOS test chip for the Combined Release and Radiation Effects Satellite (CRRES). This chip contains both analog and digital circuits. The development employed all the elements required to obtain custom circuits from silicon foundries, including circuit design, foundry interfacing, circuit test, and circuit qualification

Statistical compact model strategies for nano CMOS transistors subject of atomic scale variability

One of the major limiting factors of the CMOS device, circuit and system simulation in sub 100nm regimes is the statistical variability introduced by the discreteness of charge and granularity of matter. The statistical variability cannot be eliminated by tuning the layout or by tightening fabrication process control. Since the compact models are the key bridge between technology and design, it is necessary to transfer reliably the MOSFET statistical variability information into compact models to facilitate variability aware design practice. The aim of this project is the development of a statistical extraction methodology essential to capture statistical variability with optimum set of parameters particularly in industry standard compact model BSIM. This task is accomplished by using a detailed study on the sensitivity analysis of the transistor current in respect to key parameters in compact model in combination with error analysis of the fitted Id-Vg characteristics. The key point in the developed direct statistical compact model strategy is that the impacts of statistical variability can be captured in device characteristics by tuning a limited number of parameters and keeping the values for remaining major set equal to their default values obtained from the “uniform” MOSFET compact model extraction. However, the statistical compact model extraction strategies will accurately represent the distribution and correlation of the electrical MOSFET figures of merit. Statistical compact model parameters are generated using statistical parameter generation techniques such as uncorrelated parameter distributions, principal component analysis and nonlinear power method. The accuracy of these methods is evaluated in comparison with the results obtained from ‘atomistic’ simulations. The impact of the correlations in the compact model parameters has been analyzed along with the corresponding transistor figures of merit. The accuracy of the circuit simulations with different statistical compact model libraries has been studied. Moreover, the impact of the MOSFET width/length on the statistical trend of the optimum set of statistical compact model parameters and electrical figures of merit has been analyzed with two methods to capture geometry dependencies in proposed statistical models

Simulation of charge-trapping in nano-scale MOSFETs in the presence of random-dopants-induced variability

The growing variability of electrical characteristics is a major issue associated with continuous downscaling of contemporary bulk MOSFETs. In addition, the operating conditions brought about by these same scaling trends have pushed MOSFET degradation mechanisms such as Bias Temperature Instability (BTI) to the forefront as a critical reliability threat. This thesis investigates the impact of this ageing phenomena, in conjunction with device variability, on key MOSFET electrical parameters. A three-dimensional drift-diffusion approximation is adopted as the simulation approach in this work, with random dopant fluctuations—the dominant source of statistical variability—included in the simulations. The testbed device is a realistic 35 nm physical gate length n-channel conventional bulk MOSFET. 1000 microscopically different implementations of the transistor are simulated and subjected to charge-trapping at the oxide interface. The statistical simulations reveal relatively rare but very large threshold voltage shifts, with magnitudes over 3 times than that predicted by the conventional theoretical approach. The physical origin of this effect is investigated in terms of the electrostatic influences of the random dopants and trapped charges on the channel electron concentration. Simulations with progressively increased trapped charge densities—emulating the characteristic condition of BTI degradation—result in further variability of the threshold voltage distribution. Weak correlations of the order of 10-2 are found between the pre-degradation threshold voltage and post-degradation threshold voltage shift distributions. The importance of accounting for random dopant fluctuations in the simulations is emphasised in order to obtain qualitative agreement between simulation results and published experimental measurements. Finally, the information gained from these device-level physical simulations is integrated into statistical compact models, making the information available to circuit designers

3D drift diffusion and 3D Monte Carlo simulation of on-current variability due to random dopants

In this work Random Discrete Dopant induced on-current variations have been studied using the Glasgow 3D atomistic drift/diffusion simulator and Monte Carlo simulations. A methodology for incorporating quantum corrections into self-consistent atomistic Monte Carlo simulations via the density gradient effective potential is presented. Quantum corrections based on the density gradient formalism are used to simultaneously capture quantum confinement effects. The quantum corrections not only capture charge confinement effects, but accurately represent the electron impurity interaction used in previous \textit{ab initio} atomistic MC simulations, showing agreement with bulk mobility simulation. The effect of quantum corrected transport variation in statistical atomistic MC simulation is then investigated using a series of realistic scaled devices nMOSFETs transistors with channel lengths 35 nm, 25 nm, 18nm, 13 nm and 9 nm. Such simulations result in an increased drain current variability when compared with drift diffusion simulation. The comprehensive statistical analysis of drain current variations is presented separately for each scaled transistor. The investigation has shown increased current variation compared with quantum corrected drift diffusion simulation and with previous classical MC results. Furthermore, it has been studied consistently the impact of transport variability due to scattering from random discrete dopants on the on-current variability in realistic nano CMOS transistors. For the first time, a hierarchic simulation strategy to accurately transfer the increased on-current variability obtained from the ‘ab initio’ MC simulations to DD simulations is subsequently presented. The MC corrected DD simulations are used to produce target $I_D-V_G$ characteristics from which statistical compact models are extracted for use in preliminary design kits at the early stage of new technology development. The impact of transport variability on the accuracy of delay simulation are investigated in detail. Accurate compact models extraction methodology transferring results from accurate physical variability simulation into statistical compact models suitable for statistical circuit simulation is presented. In order to examine te size of this effect on circuits Monte Carlo SPICE simulations of inverter were carried out for 100 samples

Product assurance technology for procuring reliable, radiation-hard, custom LSI/VLSI electronics

Advanced measurement methods using microelectronic test chips are described. These chips are intended to be used in acquiring the data needed to qualify Application Specific Integrated Circuits (ASIC's) for space use. Efforts were focused on developing the technology for obtaining custom IC's from CMOS/bulk silicon foundries. A series of test chips were developed: a parametric test strip, a fault chip, a set of reliability chips, and the CRRES (Combined Release and Radiation Effects Satellite) chip, a test circuit for monitoring space radiation effects. The technical accomplishments of the effort include: (1) development of a fault chip that contains a set of test structures used to evaluate the density of various process-induced defects; (2) development of new test structures and testing techniques for measuring gate-oxide capacitance, gate-overlap capacitance, and propagation delay; (3) development of a set of reliability chips that are used to evaluate failure mechanisms in CMOS/bulk: interconnect and contact electromigration and time-dependent dielectric breakdown; (4) development of MOSFET parameter extraction procedures for evaluating subthreshold characteristics; (5) evaluation of test chips and test strips on the second CRRES wafer run; (6) two dedicated fabrication runs for the CRRES chip flight parts; and (7) publication of two papers: one on the split-cross bridge resistor and another on asymmetrical SRAM (static random access memory) cells for single-event upset analysis

Simulation study of scaling design, performance characterization, statistical variability and reliability of decananometer MOSFETs

This thesis describes a comprehensive, simulation based scaling study – including device design, performance characterization, and the impact of statistical variability – on deca-nanometer bulk MOSFETs. After careful calibration of fabrication processes and electrical characteristics for n- and p-MOSFETs with 35 nm physical gate length, 1 nm EOT and stress engineering, the simulated devices closely match the performance of contemporary 45 nm CMOS technologies. Scaling to 25 nm, 18 nm and 13 nm gate length n and p devices follows generalized scaling rules, augmented by physically realistic constraints and the introduction of high-k/metal-gate stacks. The scaled devices attain the performance stipulated by the ITRS. Device a.c. performance is analyzed, at device and circuit level. Extrinsic parasitics become critical to nano-CMOS device performance. The thesis describes device capacitance components, analyzes the CMOS inverter, and obtains new insights into the inverter propagation delay in nano-CMOS. The projection of a.c. performance of scaled devices is obtained. The statistical variability of electrical characteristics, due to intrinsic parameter fluctuation sources, in contemporary and scaled decananometer MOSFETs is systematically investigated for the first time. The statistical variability sources: random discrete dopants, gate line edge roughness and poly-silicon granularity are simulated, in combination, in an ensemble of microscopically different devices. An increasing trend in the standard deviation of the threshold voltage as a function of scaling is observed. The introduction of high-k/metal gates improves electrostatic integrity and slows this trend. Statistical evaluations of variability in Ion and Ioff as a function of scaling are also performed. For the first time, the impact of strain on statistical variability is studied. Gate line edge roughness results in areas of local channel shortening, accompanied by locally increased strain, both effects increasing the local current. Variations are observed in both the drive current, and in the drive current enhancement normally expected from the application of strain. In addition, the effects of shallow trench isolation (STI) on MOSFET performance and on its statistical variability are investigated for the first time. The inverse-narrow-width effect of STI enhances the current density adjacent to it. This leads to a local enhancement of the influence of junction shapes adjacent to the STI. There is also a statistical impact on the threshold voltage due to random STI induced traps at the silicon/oxide interface