Power Efficient Data-Aware SRAM Cell for SRAM-Based FPGA Architecture

The design of low-power SRAM cell becomes a necessity in today\u27s FPGAs, because SRAM is a critical component in FPGA design and consumes a large fraction of the total power. The present chapter provides an overview of various factors responsible for power consumption in FPGA and discusses the design techniques of low-power SRAM-based FPGA at system level, device level, and architecture levels. Finally, the chapter proposes a data-aware dynamic SRAM cell to control the power consumption in the cell. Stack effect has been adopted in the design to reduce the leakage current. The various peripheral circuits like address decoder circuit, write/read enable circuits, and sense amplifier have been modified to implement a power-efficient SRAM-based FPGA

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

GPU NTC Process Variation Compensation with Voltage Stacking

Near-threshold computing (NTC) has the potential to significantly improve efficiency in high throughput architectures, such as general-purpose computing on graphic processing unit (GPGPU). Nevertheless, NTC is more sensitive to process variation (PV) as it complicates power delivery. We propose GPU stacking, a novel method based on voltage stacking, to manage the effects of PV and improve the power delivery simultaneously. To evaluate our methodology, we first explore the design space of GPGPUs in the NTC to find a suitable baseline configuration and then apply GPU stacking to mitigate the effects of PV. When comparing with an equivalent NTC GPGPU without PV management, we achieve 37% more performance on average. When considering high production volume, our approach shifts all the chips closer to the nominal non-PV case, delivering on average (across chips) ˜80 % of the performance of nominal NTC GPGPU, whereas when not using our technique, chips would have ˜50 % of the nominal performance. We also show that our approach can be applied on top of multifrequency domain designs, improving the overall performance

Energy Aware Runtime Systems for Elastic Stream Processing Platforms

Following an invariant growth in the required computational performance of processors, the multicore revolution started around 20 years ago. This revolution was mainly an answer to power dissipation constraints restricting the increase of clock frequency in single-core processors. The multicore revolution not only brought in the challenge of parallel programming, i.e. being able to develop software exploiting the entire capabilities of manycore architectures, but also the challenge of programming heterogeneous platforms. The question of “on which processing element to map a specific computational unit?”, is well known in the embedded community. With the introduction of general-purpose graphics processing units (GPGPUs), digital signal processors (DSPs) along with many-core processors on different system-on-chip platforms, heterogeneous parallel platforms are nowadays widespread over several domains, from consumer devices to media processing platforms for telecom operators. Finding mapping together with a suitable hardware architecture is a process called design-space exploration. This process is very challenging in heterogeneous many-core architectures, which promise to offer benefits in terms of energy efficiency. The main problem is the exponential explosion of space exploration. With the recent trend of increasing levels of heterogeneity in the chip, selecting the parameters to take into account when mapping software to hardware is still an open research topic in the embedded area. For example, the current Linux scheduler has poor performance when mapping tasks to computing elements available in hardware. The only metric considered is CPU workload, which as was shown in recent work does not match true performance demands from the applications. Doing so may produce an incorrect allocation of resources, resulting in a waste of energy. The origin of this research work comes from the observation that these approaches do not provide full support for the dynamic behavior of stream processing applications, especially if these behaviors are established only at runtime. This research will contribute to the general goal of developing energy-efficient solutions to design streaming applications on heterogeneous and parallel hardware platforms. Streaming applications are nowadays widely spread in the software domain. Their distinctive characiteristic is the retrieving of multiple streams of data and the need to process them in real time. The proposed work will develop new approaches to address the challenging problem of efficient runtime coordination of dynamic applications, focusing on energy and performance management.Efter en oföränderlig tillväxt i prestandakrav hos processorer, började den flerkärniga processor-revolutionen för ungefär 20 år sedan. Denna revolution skedde till största del som en lösning till begränsningar i energieffekten allt eftersom klockfrekvensen kontinuerligt höjdes i en-kärniga processorer. Den flerkärniga processor-revolutionen medförde inte enbart utmaningen gällande parallellprogrammering, m.a.o. förmågan att utveckla mjukvara som använder sig av alla delelement i de flerkärniga processorerna, men också utmaningen med programmering av heterogena plattformar. Frågeställningen ”på vilken processorelement skall en viss beräkning utföras?” är väl känt inom ramen för inbyggda datorsystem. Efter introduktionen av grafikprocessorer för allmänna beräkningar (GPGPU), signalprocesserings-processorer (DSP) samt flerkärniga processorer på olika system-on-chip plattformar, är heterogena parallella plattformar idag omfattande inom många domäner, från konsumtionsartiklar till mediaprocesseringsplattformar för telekommunikationsoperatörer. Processen att placera beräkningarna på en passande hårdvaruplattform kallas för utforskning av en designrymd (design-space exploration). Denna process är mycket utmanande för heterogena flerkärniga arkitekturer, och kan medföra fördelar när det gäller energieffektivitet. Det största problemet är att de olika valmöjligheterna i designrymden kan växa exponentiellt. Enligt den nuvarande trenden som förespår ökad heterogeniska aspekter i processorerna är utmaningen att hitta den mest passande placeringen av beräkningarna på hårdvaran ännu en forskningsfråga inom ramen för inbyggda datorsystem. Till exempel, den nuvarande schemaläggaren i Linux operativsystemet är inkapabel att hitta en effektiv placering av beräkningarna på den underliggande hårdvaran. Det enda mätsättet som används är processorns belastning vilket, som visats i tidigare forskning, inte motsvarar den verkliga prestandan i applikationen. Användning av detta mätsätt vid resursallokering resulterar i slöseri med energi. Denna forskning härstammar från observationerna att dessa tillvägagångssätt inte stöder det dynamiska beteendet hos ström-processeringsapplikationer (stream processing applications), speciellt om beteendena bara etableras vid körtid. Denna forskning kontribuerar till det allmänna målet att utveckla energieffektiva lösningar för ström-applikationer (streaming applications) på heterogena flerkärniga hårdvaruplattformar. Ström-applikationer är numera mycket vanliga i mjukvarudomän. Deras distinkta karaktär är inläsning av flertalet dataströmmar, och behov av att processera dem i realtid. Arbetet i denna forskning understöder utvecklingen av nya sätt för att lösa det utmanade problemet att effektivt koordinera dynamiska applikationer i realtid och fokus på energi- och prestandahantering

A Construction Kit for Efficient Low Power Neural Network Accelerator Designs

Implementing embedded neural network processing at the edge requires efficient hardware acceleration that couples high computational performance with low power consumption. Driven by the rapid evolution of network architectures and their algorithmic features, accelerator designs are constantly updated and improved. To evaluate and compare hardware design choices, designers can refer to a myriad of accelerator implementations in the literature. Surveys provide an overview of these works but are often limited to system-level and benchmark-specific performance metrics, making it difficult to quantitatively compare the individual effect of each utilized optimization technique. This complicates the evaluation of optimizations for new accelerator designs, slowing-down the research progress. This work provides a survey of neural network accelerator optimization approaches that have been used in recent works and reports their individual effects on edge processing performance. It presents the list of optimizations and their quantitative effects as a construction kit, allowing to assess the design choices for each building block separately. Reported optimizations range from up to 10'000x memory savings to 33x energy reductions, providing chip designers an overview of design choices for implementing efficient low power neural network accelerators

Active timing margin management to improve microprocessor power efficiency

Improving power/performance efficiency is critical for today’s micro- processors. From edge devices to datacenters, lower power or higher performance always produces better systems, measured by lower cost of ownership or longer battery time. This thesis studies improving microprocessor power/performance efficiency by optimizing the pipeline timing margin. In particular, this thesis focuses on improving the efficacy of Active Timing Margin, a young technology that dynamically adjusts the margin. Active timing margin trims down the pipeline timing margin with a control loop that adjusts voltage and frequency based on real-time chip environment monitoring. The key insight of this thesis is that in order to maximize active timing margin’s efficiency enhancement benefits, synergistic management from processor architecture design and system software scheduling are needed. To that end, this thesis covers the major consumers of pipeline timing margin, including temperature, voltage, and process variation. For temperature variation, the thesis proposes a table-lookup based active timing margin mechanism, and an associated temperature management scheme to minimize power consumption. For voltage variation, the thesis characterizes the limiting factors of adaptive clocking’s power saving and proposes application scheduling to maximize total system power reduction. For process variation, the thesis proposes core-level adaptive clocking reconfiguration to automatically expose inter-core variation and discusses workload scheduling and throttling management to control critical application performance. The author believes the optimization presented in this thesis can potentially benefit a variety of processor architectures as the conclusions are based on the solid measurement on state-of-the-art processors, and the research objective, active timing margin, already has wide applicability in the latest microprocessors by the time this thesis is written.Electrical and Computer Engineerin

FDSOI Design using Automated Standard-Cell-Grained Body Biasing

With the introduction of FDSOI processes at competitive technology nodes, body biasing on an unprecedented scale was made possible. Body biasing influences one of the central transistor characteristics, the threshold voltage. By being able to heighten or lower threshold voltage by more than 100mV, the very physics of transistor switching can be manipulated at run time. Furthermore, as body biasing does not lead to different signal levels, it can be applied much more fine-grained than, e.g., DVFS. With the state of the art mainly focused on combinations of body biasing with DVFS, it has thus ignored granularities unfeasible for DVFS. This thesis fills this gap by proposing body bias domain partitioning techniques and for body bias domain partitionings thereby generated, algorithms that search for body bias assignments. Several different granularities ranging from entire cores to small groups of standard cells were examined using two principal approaches: Designer aided pre-partitioning based determination of body bias domains and a first-time, fully automatized, netlist based approach called domain candidate exploration. Both approaches operate along the lines of activation and timing of standard cell groups. These approaches were evaluated using the example of a Dynamically Reconfigurable Processor (DRP), a highly efficient category of reconfigurable architectures which consists of an array of processing elements and thus offers many opportunities for generalization towards many-core architectures. Finally, the proposed methods were validated by manufacturing a test-chip. Extensive simulation runs as well as the test-chip evaluation showed the validity of the proposed methods and indicated substantial improvements in energy efficiency compared to the state of the art. These improvements were accomplished by the fine-grained partitioning of the DRP design. This method allowed reducing dynamic power through supply voltage levels yielding higher clock frequencies using forward body biasing, while simultaneously reducing static power consumption in unused parts.Die Einführung von FDSOI Prozessen in gegenwärtigen Prozessgrößen ermöglichte die Nutzung von Substratvorspannung in nie zuvor dagewesenem Umfang. Substratvorspannung beeinflusst unter anderem eine zentrale Eigenschaft von Transistoren, die Schwellspannung. Mittels Substratvorspannung kann diese um mehr als 100mV erhöht oder gesenkt werden, was es ermöglicht, die schiere Physik des Schaltvorgangs zu manipulieren. Da weiterhin hiervon der Signalpegel der digitalen Signale unberührt bleibt, kann diese Technik auch in feineren Granularitäten angewendet werden, als z.B. Dynamische Spannungs- und Frequenz Anpassung (Engl. Dynamic Voltage and Frequency Scaling, Abk. DVFS). Da jedoch der Stand der Technik Substratvorspannung hauptsächlich in Kombinationen mit DVFS anwendet, werden feinere Granularitäten, welche für DVFS nicht mehr wirtschaftlich realisierbar sind, nicht berücksichtigt. Die vorliegende Arbeit schließt diese Lücke, indem sie Partitionierungsalgorithmen zur Unterteilung eines Entwurfs in Substratvorspannungsdomänen vorschlägt und für diese hierdurch unterteilten Domänen entsprechende Substratvorspannungen berechnet. Hierzu wurden verschiedene Granularitäten berücksichtigt, von ganzen Prozessorkernen bis hin zu kleinen Gruppen von Standardzellen. Diese Entwürfe wurden dann mit zwei verschiedenen Herangehensweisen unterteilt: Chipdesigner unterstützte, vorpartitionierungsbasierte Bestimmung von Substratvorspannungsdomänen, sowie ein erstmals vollautomatisierter, Netzlisten basierter Ansatz, in dieser Arbeit Domänen Kandidaten Exploration genannt. Beide Ansätze funktionieren nach dem Prinzip der Aktivierung, d.h. zu welchem Zeitpunkt welcher Teil des Entwurfs aktiv ist, sowie der Signallaufzeit durch die entsprechenden Entwurfsteile. Diese Ansätze wurden anhand des Beispiels Dynamisch Rekonfigurierbarer Prozessoren (DRP) evaluiert. DRPs stellen eine Klasse hocheffizienter rekonfigurierbarer Architekturen dar, welche hauptsächlich aus einem Feld von Rechenelementen besteht und dadurch auch zahlreiche Möglichkeiten zur Verallgemeinerung hinsichtlich Many-Core Architekturen zulässt. Schließlich wurden die vorgeschlagenen Methoden in einem Testchip validiert. Alle ermittelten Ergebnisse zeigen im Vergleich zum Stand der Technik drastische Verbesserungen der Energieeffizienz, welche durch die feingranulare Unterteilung in Substratvorspannungsdomänen erzielt wurde. Hierdurch konnten durch die Anwendung von Substratvorspannung höhere Taktfrequenzen bei gleicher Versorgungsspannung erzielt werden, während zeitgleich in zeitlich unkritischen oder ungenutzten Entwurfsteilen die statische Leistungsaufnahme minimiert wurde

Radiation Hardened by Design Methodologies for Soft-Error Mitigated Digital Architectures

abstract: Digital architectures for data encryption, processing, clock synthesis, data transfer, etc. are susceptible to radiation induced soft errors due to charge collection in complementary metal oxide semiconductor (CMOS) integrated circuits (ICs). Radiation hardening by design (RHBD) techniques such as double modular redundancy (DMR) and triple modular redundancy (TMR) are used for error detection and correction respectively in such architectures. Multiple node charge collection (MNCC) causes domain crossing errors (DCE) which can render the redundancy ineffectual. This dissertation describes techniques to ensure DCE mitigation with statistical confidence for various designs. Both sequential and combinatorial logic are separated using these custom and computer aided design (CAD) methodologies. Radiation vulnerability and design overhead are studied on VLSI sub-systems including an advanced encryption standard (AES) which is DCE mitigated using module level coarse separation on a 90-nm process with 99.999% DCE mitigation. A radiation hardened microprocessor (HERMES2) is implemented in both 90-nm and 55-nm technologies with an interleaved separation methodology with 99.99% DCE mitigation while achieving 4.9% increased cell density, 28.5 % reduced routing and 5.6% reduced power dissipation over the module fences implementation. A DMR register-file (RF) is implemented in 55 nm process and used in the HERMES2 microprocessor. The RF array custom design and the decoders APR designed are explored with a focus on design cycle time. Quality of results (QOR) is studied from power, performance, area and reliability (PPAR) perspective to ascertain the improvement over other design techniques. A radiation hardened all-digital multiplying pulsed digital delay line (DDL) is designed for double data rate (DDR2/3) applications for data eye centering during high speed off-chip data transfer. The effect of noise, radiation particle strikes and statistical variation on the designed DDL are studied in detail. The design achieves the best in class 22.4 ps peak-to-peak jitter, 100-850 MHz range at 14 pJ/cycle energy consumption. Vulnerability of the non-hardened design is characterized and portions of the redundant DDL are separated in custom and auto-place and route (APR). Thus, a range of designs for mission critical applications are implemented using methodologies proposed in this work and their potential PPAR benefits explored in detail.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering