The Integration of nearthreshold and subthreshold CMOS logic for energy minimization

With the rapid growth in the use of portable electronic devices, more emphasis has recently been placed on low-energy circuit design. Digital subthreshold complementary metal-oxide-semiconductor (CMOS) circuit design is one area of study that offers significant energy reduction by operating at a supply voltage substantially lower than the threshold voltage of the transistor. However, these energy savings come at a critical cost to performance, restricting its use to severely energy-constrained applications such as microsensor nodes. In an effort to mitigate this performance degradation in low-energy designs, nearthreshold circuit design has been proposed and implemented in digital circuits such as Intel\u27s energy-efficient hardware accelerator. The application spectrum of nearthreshold and subthreshold design could be broadened by integrating these cells into high-performance designs. This research focuses on the integration of characterized nearthreshold and subthreshold standard cells into high-performance functional modules. Within these functional modules, energy minimization is achieved while satisfying performance constraints by replacing non-critical path logic with nearthreshold and subthreshold logic cells. Specifically, the critical path method is used to bind the timing and energy constraints of the design. The design methodology was verified and tested with several benchmark circuits, including a cryptographic hash function, Skein. An average energy savings of 41.15% was observed at a circuit performance degradation factor of 10. The energy overhead of the level shifters accounted for at least 8.5% of the energy consumption of the optimized circuit, with an average energy overhead of 26.76%. A heuristic approach is developed for estimating the energy savings of the optimized design

Designing energy-efficient sub-threshold logic circuits using equalization and non-volatile memory circuits using memristors

The very large scale integration (VLSI) community has utilized aggressive complementary metal-oxide semiconductor (CMOS) technology scaling to meet the ever-increasing performance requirements of computing systems. However, as we enter the nanoscale regime, the prevalent process variation effects degrade the CMOS device reliability. Hence, it is increasingly essential to explore emerging technologies which are compatible with the conventional CMOS process for designing highly-dense memory/logic circuits. Memristor technology is being explored as a potential candidate in designing non-volatile memory arrays and logic circuits with high density, low latency and small energy consumption. In this thesis, we present the detailed functionality of multi-bit 1-Transistor 1-memRistor (1T1R) cell-based memory arrays. We present the performance and energy models for an individual 1T1R memory cell and the memory array as a whole. We have considered TiO2- and HfOx-based memristors, and for these technologies there is a sub-10% difference between energy and performance computed using our models and HSPICE simulations. Using a performance-driven design approach, the energy-optimized TiO2-based RRAM array consumes the least write energy (4.06 pJ/bit) and read energy (188 fJ/bit) when storing 3 bits/cell for 100 nsec write and 1 nsec read access times. Similarly, HfOx-based RRAM array consumes the least write energy (365 fJ/bit) and read energy (173 fJ/bit) when storing 3 bits/cell for 1 nsec write and 200 nsec read access times. On the logic side, we investigate the use of equalization techniques to improve the energy efficiency of digital sequential logic circuits in sub-threshold regime. We first propose the use of a variable threshold feedback equalizer circuit with combinational logic blocks to mitigate the timing errors in digital logic designed in sub-threshold regime. This mitigation of timing errors can be leveraged to reduce the dominant leakage energy by scaling supply voltage or decreasing the propagation delay. At the fixed supply voltage, we can decrease the propagation delay of the critical path in a combinational logic block using equalizer circuits and, correspondingly decrease the leakage energy consumption. For a 8-bit carry lookahead adder designed in UMC 130 nm process, the operating frequency can be increased by 22.87% (on average), while reducing the leakage energy by 22.6% (on average) in the sub-threshold regime. Overall, the feedback equalization technique provides up to 35.4% lower energy-delay product compared to the conventional non-equalized logic. We also propose a tunable adaptive feedback equalizer circuit that can be used with sequential digital logic to mitigate the process variation effects and reduce the dominant leakage energy component in sub-threshold digital logic circuits. For a 64-bit adder designed in 130 nm our proposed approach can reduce the normalized delay variation of the critical path delay from 16.1% to 11.4% while reducing the energy-delay product by 25.83% at minimum energy supply voltage. In addition, we present detailed energy-performance models of the adaptive feedback equalizer circuit. This work serves as a foundation for the design of robust, energy-efficient digital logic circuits in sub-threshold regime

Spin-Based Neuron Model with Domain Wall Magnets as Synapse

We present artificial neural network design using spin devices that achieves ultra low voltage operation, low power consumption, high speed, and high integration density. We employ spin torque switched nano-magnets for modelling neuron and domain wall magnets for compact, programmable synapses. The spin based neuron-synapse units operate locally at ultra low supply voltage of 30mV resulting in low computation power. CMOS based inter-neuron communication is employed to realize network-level functionality. We corroborate circuit operation with physics based models developed for the spin devices. Simulation results for character recognition as a benchmark application shows 95% lower power consumption as compared to 45nm CMOS design

Graphene-based current mode logic circuits: a simulation study for an emerging technology

In this paper, the usage of graphene transistors is introduced to be a suitable solution for extending low power designs. Static and current mode logic (CML) styles on both nanoscale graphene and silicon FINFET technologies are compared. Results show that power in CML styles approximately are independent of frequency and the graphene-based CML (G-CML) designs are more power-efficient as the frequency and complexity increase. Compared to silicon-based CML (Si-CML) standard cells, there is 94% reduction in power consumption for G-CML counterparts. Furthermore, a G-CML 4-bit adder respectively offers 8.9 and 1.7 times less power and delay than the Si-CML adder

Ultra Low Power Digital Circuit Design for Wireless Sensor Network Applications

Ny forskning innenfor feltet trådløse sensornettverk åpner for nye og innovative produkter og løsninger. Biomedisinske anvendelser er blant områdene med størst potensial og det investeres i dag betydelige beløp for å bruke denne teknologien for å gjøre medisinsk diagnostikk mer effektiv samtidig som man åpner for fjerndiagnostikk basert på trådløse sensornoder integrert i et ”helsenett”. Målet er å forbedre tjenestekvalitet og redusere kostnader samtidig som brukerne skal oppleve forbedret livskvalitet som følge av økt trygghet og mulighet for å tilbringe mest mulig tid i eget hjem og unngå unødvendige sykehusbesøk og innleggelser. For å gjøre dette til en realitet er man avhengige av sensorelektronikk som bruker minst mulig energi slik at man oppnår tilstrekkelig batterilevetid selv med veldig små batterier. I sin avhandling ” Ultra Low power Digital Circuit Design for Wireless Sensor Network Applications” har PhD-kandidat Farshad Moradi fokusert på nye løsninger innenfor konstruksjon av energigjerrig digital kretselektronikk. Avhandlingen presenterer nye løsninger både innenfor aritmetiske og kombinatoriske kretser, samtidig som den studerer nye statiske minneelementer (SRAM) og alternative minnearkitekturer. Den ser også på utfordringene som oppstår når silisiumteknologien nedskaleres i takt med mikroprosessorutviklingen og foreslår løsninger som bidrar til å gjøre kretsløsninger mer robuste og skalerbare i forhold til denne utviklingen. De viktigste konklusjonene av arbeidet er at man ved å introdusere nye konstruksjonsteknikker både er i stand til å redusere energiforbruket samtidig som robusthet og teknologiskalerbarhet øker. Forskningen har vært utført i samarbeid med Purdue University og vært finansiert av Norges Forskningsråd gjennom FRINATprosjektet ”Micropower Sensor Interface in Nanometer CMOS Technology”

Phase Noise Analyses and Measurements in the Hybrid Memristor-CMOS Phase-Locked Loop Design and Devices Beyond Bulk CMOS

Phase-locked loop (PLLs) has been widely used in analog or mixed-signal integrated circuits. Since there is an increasing market for low noise and high speed devices, PLLs are being employed in communications. In this dissertation, we investigated phase noise, tuning range, jitter, and power performances in different architectures of PLL designs. More energy efficient devices such as memristor, graphene, transition metal di-chalcogenide (TMDC) materials and their respective transistors are introduced in the design phase-locked loop. Subsequently, we modeled phase noise of a CMOS phase-locked loop from the superposition of noises from its building blocks which comprises of a voltage-controlled oscillator, loop filter, frequency divider, phase-frequency detector, and the auxiliary input reference clock. Similarly, a linear time-invariant model that has additive noise sources in frequency domain is used to analyze the phase noise. The modeled phase noise results are further compared with the corresponding phase-locked loop designs in different n-well CMOS processes. With the scaling of CMOS technology and the increase of the electrical field, the problem of short channel effects (SCE) has become dominant, which causes decay in subthreshold slope (SS) and positive and negative shifts in the threshold voltages of nMOS and pMOS transistors, respectively. Various devices are proposed to continue extending Moore\u27s law and the roadmap in semiconductor industry. We employed tunnel field effect transistor owing to its better performance in terms of SS, leakage current, power consumption etc. Applying an appropriate bias voltage to the gate-source region of TFET causes the valence band to align with the conduction band and injecting the charge carriers. Similarly, under reverse bias, the two bands are misaligned and there is no injection of carriers. We implemented graphene TFET and MoS2 in PLL design and the results show improvements in phase noise, jitter, tuning range, and frequency of operation. In addition, the power consumption is greatly reduced due to the low supply voltage of tunnel field effect transistor

Designing energy-efficient computing systems using equalization and machine learning

As technology scaling slows down in the nanometer CMOS regime and mobile computing becomes more ubiquitous, designing energy-efficient hardware for mobile systems is becoming increasingly critical and challenging. Although various approaches like near-threshold computing (NTC), aggressive voltage scaling with shadow latches, etc. have been proposed to get the most out of limited battery life, there is still no “silver bullet” to increasing power-performance demands of the mobile systems. Moreover, given that a mobile system could operate in a variety of environmental conditions, like different temperatures, have varying performance requirements, etc., there is a growing need for designing tunable/reconfigurable systems in order to achieve energy-efficient operation. In this work we propose to address the energy- efficiency problem of mobile systems using two different approaches: circuit tunability and distributed adaptive algorithms. Inspired by the communication systems, we developed feedback equalization based digital logic that changes the threshold of its gates based on the input pattern. We showed that feedback equalization in static complementary CMOS logic enabled up to 20% reduction in energy dissipation while maintaining the performance metrics. We also achieved 30% reduction in energy dissipation for pass-transistor digital logic (PTL) with equalization while maintaining performance. In addition, we proposed a mechanism that leverages feedback equalization techniques to achieve near optimal operation of static complementary CMOS logic blocks over the entire voltage range from near threshold supply voltage to nominal supply voltage. Using energy-delay product (EDP) as a metric we analyzed the use of the feedback equalizer as part of various sequential computational blocks. Our analysis shows that for near-threshold voltage operation, when equalization was used, we can improve the operating frequency by up to 30%, while the energy increase was less than 15%, with an overall EDP reduction of ≈10%. We also observe an EDP reduction of close to 5% across entire above-threshold voltage range. On the distributed adaptive algorithm front, we explored energy-efficient hardware implementation of machine learning algorithms. We proposed an adaptive classifier that leverages the wide variability in data complexity to enable energy-efficient data classification operations for mobile systems. Our approach takes advantage of varying classification hardness across data to dynamically allocate resources and improve energy efficiency. On average, our adaptive classifier is ≈100× more energy efficient but has ≈1% higher error rate than a complex radial basis function classifier and is ≈10× less energy efficient but has ≈40% lower error rate than a simple linear classifier across a wide range of classification data sets. We also developed a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) under tight energy budgets. The FoG architecture takes advantage of the fact that in random forests a small portion of the weak classifiers (decision trees) might be sufficient to achieve high statistical performance. By dividing the random forest into smaller forests (Groves), and conditionally executing the rest of the forest, FoG is able to achieve much higher energy efficiency levels for comparable error rates. We also take advantage of the distributed nature of the FoG to achieve high level of parallelism. Our evaluation shows that at maximum achievable accuracies FoG consumes ≈1.48×, ≈24×, ≈2.5×, and ≈34.7× lower energy per classification compared to conventional RF, SVM-RBF , Multi-Layer Perceptron Network (MLP), and CNN, respectively. FoG is 6.5× less energy efficient than SVM-LR, but achieves 18% higher accuracy on average across all considered datasets

Energy efficient hybrid computing systems using spin devices

Emerging spin-devices like magnetic tunnel junctions (MTJ\u27s), spin-valves and domain wall magnets (DWM) have opened new avenues for spin-based logic design. This work explored potential computing applications which can exploit such devices for higher energy-efficiency and performance. The proposed applications involve hybrid design schemes, where charge-based devices supplement the spin-devices, to gain large benefits at the system level. As an example, lateral spin valves (LSV) involve switching of nanomagnets using spin-polarized current injection through a metallic channel such as Cu. Such spin-torque based devices possess several interesting properties that can be exploited for ultra-low power computation. Analog characteristic of spin current facilitate non-Boolean computation like majority evaluation that can be used to model a neuron. The magneto-metallic neurons can operate at ultra-low terminal voltage of ∼20mV, thereby resulting in small computation power. Moreover, since nano-magnets inherently act as memory elements, these devices can facilitate integration of logic and memory in interesting ways. The spin based neurons can be integrated with CMOS and other emerging devices leading to different classes of neuromorphic/non-Von-Neumann architectures. The spin-based designs involve `mixed-mode\u27 processing and hence can provide very compact and ultra-low energy solutions for complex computation blocks, both digital as well as analog. Such low-power, hybrid designs can be suitable for various data processing applications like cognitive computing, associative memory, and currentmode on-chip global interconnects. Simulation results for these applications based on device-circuit co-simulation framework predict more than ∼100x improvement in computation energy as compared to state of the art CMOS design, for optimal spin-device parameters