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

ULTRA ENERGY-EFFICIENT SUB-/NEAR-THRESHOLD COMPUTING: PLATFORM AND METHODOLOGY

Ph.DDOCTOR OF PHILOSOPH

Energy-Aware Data Movement In Non-Volatile Memory Hierarchies

While technology scaling enables increased density for memory cells, the intrinsic high leakage power of conventional CMOS technology and the demand for reduced energy consumption inspires the use of emerging technology alternatives such as eDRAM and Non-Volatile Memory (NVM) including STT-MRAM, PCM, and RRAM. The utilization of emerging technology in Last Level Cache (LLC) designs which occupies a signifcant fraction of total die area in Chip Multi Processors (CMPs) introduces new dimensions of vulnerability, energy consumption, and performance delivery. To be specific, a part of this research focuses on eDRAM Bit Upset Vulnerability Factor (BUVF) to assess vulnerable portion of the eDRAM refresh cycle where the critical charge varies depending on the write voltage, storage and bit-line capacitance. This dissertation broaden the study on vulnerability assessment of LLC through investigating the impact of Process Variations (PV) on narrow resistive sensing margins in high-density NVM arrays, including on-chip cache and primary memory. Large-latency and power-hungry Sense Amplifers (SAs) have been adapted to combat PV in the past. Herein, a novel approach is proposed to leverage the PV in NVM arrays using Self-Organized Sub-bank (SOS) design. SOS engages the preferred SA alternative based on the intrinsic as-built behavior of the resistive sensing timing margin to reduce the latency and power consumption while maintaining acceptable access time. On the other hand, this dissertation investigates a novel technique to prioritize the service to 1) Extensive Read Reused Accessed blocks of the LLC that are silently dropped from higher levels of cache, and 2) the portion of the working set that may exhibit distant re-reference interval in L2. In particular, we develop a lightweight Multi-level Access History Profiler to effciently identify ERRA blocks through aggregating the LLC block addresses tagged with identical Most Signifcant Bits into a single entry. Experimental results indicate that the proposed technique can reduce the L2 read miss ratio by 51.7% on average across PARSEC and SPEC2006 workloads. In addition, this dissertation will broaden and apply advancements in theories of subspace recovery to pioneer computationally-aware in-situ operand reconstruction via the novel Logic In Interconnect (LI2) scheme. LI2 will be developed, validated, and re?ned both theoretically and experimentally to realize a radically different approach to post-Moore\u27s Law computing by leveraging low-rank matrices features offering data reconstruction instead of fetching data from main memory to reduce energy/latency cost per data movement. We propose LI2 enhancement to attain high performance delivery in the post-Moore\u27s Law era through equipping the contemporary micro-architecture design with a customized memory controller which orchestrates the memory request for fetching low-rank matrices to customized Fine Grain Reconfigurable Accelerator (FGRA) for reconstruction while the other memory requests are serviced as before. The goal of LI2 is to conquer the high latency/energy required to traverse main memory arrays in the case of LLC miss, by using in-situ construction of the requested data dealing with low-rank matrices. Thus, LI2 exchanges a high volume of data transfers with a novel lightweight reconstruction method under specific conditions using a cross-layer hardware/algorithm approach

Energy-Efficient System Architectures for Intermittently-Powered IoT Devices

Various industry forecasts project that, by 2020, there will be around 50 billion devices connected to the Internet of Things (IoT), helping to engineer new solutions to societal-scale problems such as healthcare, energy conservation, transportation, etc. Most of these devices will be wireless due to the expense, inconvenience, or in some cases, the sheer infeasibility of wiring them. With no cord for power and limited space for a battery, powering these devices for operating in a set-and-forget mode (i.e., achieve several months to possibly years of unattended operation) becomes a daunting challenge. Environmental energy harvesting (where the system powers itself using energy that it scavenges from its operating environment) has been shown to be a promising and viable option for powering these IoT devices. However, ambient energy sources (such as vibration, wind, RF signals) are often minuscule, unreliable, and intermittent in nature, which can lead to frequent intervals of power loss. Performing computations reliably in the face of such power supply interruptions is challenging

Robust Circuit Design for Low-Voltage VLSI.

Voltage scaling is an effective way to reduce the overall power consumption, but the major challenges in low voltage operations include performance degradation and reliability issues due to PVT variations. This dissertation discusses three key circuit components that are critical in low-voltage VLSI. Level converters must be a reliable interface between two voltage domains, but the reduced on/off-current ratio makes it extremely difficult to achieve robust conversions at low voltages. Two static designs are proposed: LC2 adopts a novel pulsed-operation and modulates its pull-up strength depending on its state. A 3-sigma robustness is guaranteed using a current margin plot; SLC inherently reduces the contention by diode-insertion. Improvements in performance, power, and robustness are measured from 130nm CMOS test chips. SRAM is a major bottleneck in voltage-scaling due to its inherent ratioed-bitcell design. The proposed 7T SRAM alleviates the area overhead incurred by 8T bitcells and provides robust operation down to 0.32V in 180nm CMOS test chips with 3.35fW/bit leakage. Auto-Shut-Off provides a 6.8x READ energy reduction, and its innate Quasi-Static READ has been demonstrated which shows a much improved READ error rate. A use of PMOS Pass-Gate improves the half-select robustness by directly modulating the device strength through bitline voltage. Clocked sequential elements, flip-flops in short, are ubiquitous in today’s digital systems. The proposed S2CFF is static, single-phase, contention-free, and has the same number of devices as in TGFF. It shows a 40% power reduction as well as robust low-voltage operations in fabricated 45nm SOI test chips. Its simple hold-time path and the 3.4x improvement in 3-sigma hold-time is presented. A new on-chip flip-flop testing harness is also proposed, and measured hold-time variations of flip-flops are presented.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111525/1/yejoong_1.pd

MFPA: Mixed-Signal Field Programmable Array for Energy-Aware Compressive Signal Processing

Compressive Sensing (CS) is a signal processing technique which reduces the number of samples taken per frame to decrease energy, storage, and data transmission overheads, as well as reducing time taken for data acquisition in time-critical applications. The tradeoff in such an approach is increased complexity of signal reconstruction. While several algorithms have been developed for CS signal reconstruction, hardware implementation of these algorithms is still an area of active research. Prior work has sought to utilize parallelism available in reconstruction algorithms to minimize hardware overheads; however, such approaches are limited by the underlying limitations in CMOS technology. Herein, the MFPA (Mixed-signal Field Programmable Array) approach is presented as a hybrid spin-CMOS reconfigurable fabric specifically designed for implementation of CS data sampling and signal reconstruction. The resulting fabric consists of 1) slice-organized analog blocks providing amplifiers, transistors, capacitors, and Magnetic Tunnel Junctions (MTJs) which are configurable to achieving square/square root operations required for calculating vector norms, 2) digital functional blocks which feature 6-input clockless lookup tables for computation of matrix inverse, and 3) an MRAM-based nonvolatile crossbar array for carrying out low-energy matrix-vector multiplication operations. The various functional blocks are connected via a global interconnect and spin-based analog-to-digital converters. Simulation results demonstrate significant energy and area benefits compared to equivalent CMOS digital implementations for each of the functional blocks used: this includes an 80% reduction in energy and 97% reduction in transistor count for the nonvolatile crossbar array, 80% standby power reduction and 25% reduced area footprint for the clockless lookup tables, and roughly 97% reduction in transistor count for a multiplier built using components from the analog blocks. Moreover, the proposed fabric yields 77% energy reduction compared to CMOS when used to implement CS reconstruction, in addition to latency improvements

Hardware/Software Co-Design of Ultra-Low Power Biomedical Monitors

Ongoing changes in world demographics and the prevalence of unhealthy lifestyles are imposing a paradigm shift in healthcare delivery. Nowadays, chronic ailments such as cardiovascular diseases, hypertension and diabetes, represent the most common causes of death according to the World Health Organization. It is estimated that 63% of deaths worldwide are directly or indirectly related to these non-communicable diseases (NCDs), and by 2030 it is predicted that the health delivery cost will reach an amount comparable to 75% of the current GDP. In this context, technologies based on Wireless Sensor Nodes (WSNs) effectively alleviate this burden enabling the conception of wearable biomedical monitors composed of one or several devices connected through a Wireless Body Sensor Network (WBSN). Energy efficiency is of paramount importance for these devices, which must operate for prolonged periods of time with a single battery charge. In this thesis I propose a set of hardware/software co-design techniques to drastically increase the energy efficiency of bio-medical monitors. To this end, I jointly explore different alternatives to reduce the required computational effort at the software level while optimizing the power consumption of the processing hardware by employing ultra-low power multi-core architectures that exploit DSP application characteristics. First, at the sensor level, I study the utilization of a heartbeat classifier to perform selective advanced DSP on state-of-the-art ECG bio-medical monitors. To this end, I developed a framework to design and train real-time, lightweight heartbeat neuro-fuzzy classifiers, detail- ing the required optimizations to efficiently execute them on a resource-constrained platform. Then, at the network level I propose a more complex transmission-aware WBSN for activity monitoring that provides different tradeoffs between classification accuracy and transmission volume. In this work, I study the combination of a minimal set of WSNs with a smartphone, and propose two classification schemes that trade accuracy for transmission volume. The proposed method can achieve accuracies ranging from 88% to 97% and can save up to 86% of wireless transmissions, outperforming the state-of-the-art alternatives. Second, I propose a synchronization-based low-power multi-core architecture for bio-signal processing. I introduce a hardware/software synchronization mechanism that allows to achieve high energy efficiency while parallelizing the execution of multi-channel DSP applications. Then, I generalize the methodology to support bio-signal processing applications with an arbitrarily high degree of parallelism. Due to the benefits of SIMD execution and software pipelining, the architecture can reduce its power consumption by up 38% when compared to an equivalent low-power single-core alternative. Finally, I focused on the optimization of the multi-core memory subsystem, which is the major contributor to the overall system power consumption. First I considered a hybrid memory subsystem featuring a small reliable partition that can operate at ultra-low voltage enabling low-power buffering of data and obtaining up to 50% energy savings. Second, I explore a two-level memory hierarchy based on non-volatile memories (NVM) that allows for aggressive fine-grained power gating enabled by emerging low-power NVM technologies and monolithic 3D integration. Experimental results show that, by adopting this memory hierarchy, power consumption can be reduced by 5.42x in the DSP stage

Gestión de jerarquías de memoria híbridas a nivel de sistema

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadoras y Automática y de Ku Leuven, Arenberg Doctoral School, Faculty of Engineering Science, leída el 11/05/2017.In electronics and computer science, the term ‘memory’ generally refers to devices that are used to store information that we use in various appliances ranging from our PCs to all hand-held devices, smart appliances etc. Primary/main memory is used for storage systems that function at a high speed (i.e. RAM). The primary memory is often associated with addressable semiconductor memory, i.e. integrated circuits consisting of silicon-based transistors, used for example as primary memory but also other purposes in computers and other digital electronic devices. The secondary/auxiliary memory, in comparison provides program and data storage that is slower to access but offers larger capacity. Examples include external hard drives, portable flash drives, CDs, and DVDs. These devices and media must be either plugged in or inserted into a computer in order to be accessed by the system. Since secondary storage technology is not always connected to the computer, it is commonly used for backing up data. The term storage is often used to describe secondary memory. Secondary memory stores a large amount of data at lesser cost per byte than primary memory; this makes secondary storage about two orders of magnitude less expensive than primary storage. There are two main types of semiconductor memory: volatile and nonvolatile. Examples of non-volatile memory are ‘Flash’ memory (sometimes used as secondary, sometimes primary computer memory) and ROM/PROM/EPROM/EEPROM memory (used for firmware such as boot programs). Examples of volatile memory are primary memory (typically dynamic RAM, DRAM), and fast CPU cache memory (typically static RAM, SRAM, which is fast but energy-consuming and offer lower memory capacity per are a unit than DRAM). Non-volatile memory technologies in Si-based electronics date back to the 1990s. Flash memory is widely used in consumer electronic products such as cellphones and music players and NAND Flash-based solid-state disks (SSDs) are increasingly displacing hard disk drives as the primary storage device in laptops, desktops, and even data centers. The integration limit of Flash memories is approaching, and many new types of memory to replace conventional Flash memories have been proposed. The rapid increase of leakage currents in Silicon CMOS transistors with scaling poses a big challenge for the integration of SRAM memories. There is also the case of susceptibility to read/write failure with low power schemes. As a result of this, over the past decade, there has been an extensive pooling of time, resources and effort towards developing emerging memory technologies like Resistive RAM (ReRAM/RRAM), STT-MRAM, Domain Wall Memory and Phase Change Memory(PRAM). Emerging non-volatile memory technologies promise new memories to store more data at less cost than the expensive-to build silicon chips used by popular consumer gadgets including digital cameras, cell phones and portable music players. These new memory technologies combine the speed of static random-access memory (SRAM), the density of dynamic random-access memory (DRAM), and the non-volatility of Flash memory and so become very attractive as another possibility for future memory hierarchies. The research and information on these Non-Volatile Memory (NVM) technologies has matured over the last decade. These NVMs are now being explored thoroughly nowadays as viable replacements for conventional SRAM based memories even for the higher levels of the memory hierarchy. Many other new classes of emerging memory technologies such as transparent and plastic, three-dimensional(3-D), and quantum dot memory technologies have also gained tremendous popularity in recent years...En el campo de la informática, el término ‘memoria’ se refiere generalmente a dispositivos que son usados para almacenar información que posteriormente será usada en diversos dispositivos, desde computadoras personales (PC), móviles, dispositivos inteligentes, etc. La memoria principal del sistema se utiliza para almacenar los datos e instrucciones de los procesos que se encuentre en ejecución, por lo que se requiere que funcionen a alta velocidad (por ejemplo, DRAM). La memoria principal está implementada habitualmente mediante memorias semiconductoras direccionables, siendo DRAM y SRAM los principales exponentes. Por otro lado, la memoria auxiliar o secundaria proporciona almacenaje(para ficheros, por ejemplo); es más lenta pero ofrece una mayor capacidad. Ejemplos típicos de memoria secundaria son discos duros, memorias flash portables, CDs y DVDs. Debido a que estos dispositivos no necesitan estar conectados a la computadora de forma permanente, son muy utilizados para almacenar copias de seguridad. La memoria secundaria almacena una gran cantidad de datos aun coste menor por bit que la memoria principal, siendo habitualmente dos órdenes de magnitud más barata que la memoria primaria. Existen dos tipos de memorias de tipo semiconductor: volátiles y no volátiles. Ejemplos de memorias no volátiles son las memorias Flash (algunas veces usadas como memoria secundaria y otras veces como memoria principal) y memorias ROM/PROM/EPROM/EEPROM (usadas para firmware como programas de arranque). Ejemplos de memoria volátil son las memorias DRAM (RAM dinámica), actualmente la opción predominante a la hora de implementar la memoria principal, y las memorias SRAM (RAM estática) más rápida y costosa, utilizada para los diferentes niveles de cache. Las tecnologías de memorias no volátiles basadas en electrónica de silicio se remontan a la década de1990. Una variante de memoria de almacenaje por carga denominada como memoria Flash es mundialmente usada en productos electrónicos de consumo como telefonía móvil y reproductores de música mientras NAND Flash solid state disks(SSDs) están progresivamente desplazando a los dispositivos de disco duro como principal unidad de almacenamiento en computadoras portátiles, de escritorio e incluso en centros de datos. En la actualidad, hay varios factores que amenazan la actual predominancia de memorias semiconductoras basadas en cargas (capacitivas). Por un lado, se está alcanzando el límite de integración de las memorias Flash, lo que compromete su escalado en el medio plazo. Por otra parte, el fuerte incremento de las corrientes de fuga de los transistores de silicio CMOS actuales, supone un enorme desafío para la integración de memorias SRAM. Asimismo, estas memorias son cada vez más susceptibles a fallos de lectura/escritura en diseños de bajo consumo. Como resultado de estos problemas, que se agravan con cada nueva generación tecnológica, en los últimos años se han intensificado los esfuerzos para desarrollar nuevas tecnologías que reemplacen o al menos complementen a las actuales. Los transistores de efecto campo eléctrico ferroso (FeFET en sus siglas en inglés) se consideran una de las alternativas más prometedores para sustituir tanto a Flash (por su mayor densidad) como a DRAM (por su mayor velocidad), pero aún está en una fase muy inicial de su desarrollo. Hay otras tecnologías algo más maduras, en el ámbito de las memorias RAM resistivas, entre las que cabe destacar ReRAM (o RRAM), STT-RAM, Domain Wall Memory y Phase Change Memory (PRAM)...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEunpu

Design and analysis of SRAMs for energy harvesting systems

PhD ThesisAt present, the battery is employed as a power source for wide varieties of microelectronic systems ranging from biomedical implants and sensor net-works to portable devices. However, the battery has several limitations and incurs many challenges for the majority of these systems. For instance, the design considerations of implantable devices concern about the battery from two aspects, the toxic materials it contains and its lifetime since replacing the battery means a surgical operation. Another challenge appears in wire-less sensor networks, where hundreds or thousands of nodes are scattered around the monitored environment and the battery of each node should be maintained and replaced regularly, nonetheless, the batteries in these nodes do not all run out at the same time. Since the introduction of portable systems, the area of low power designs has witnessed extensive research, driven by the industrial needs, towards the aim of extending the lives of batteries. Coincidentally, the continuing innovations in the field of micro-generators made their outputs in the same range of several portable applications. This overlap creates a clear oppor-tunity to develop new generations of electronic systems that can be powered, or at least augmented, by energy harvesters. Such self-powered systems benefit applications where maintaining and replacing batteries are impossi-ble, inconvenient, costly, or hazardous, in addition to decreasing the adverse effects the battery has on the environment. The main goal of this research study is to investigate energy harvesting aware design techniques for computational logic in order to enable the capa- II bility of working under non-deterministic energy sources. As a case study, the research concentrates on a vital part of all computational loads, SRAM, which occupies more than 90% of the chip area according to the ITRS re-ports. Essentially, this research conducted experiments to find out the design met-ric of an SRAM that is the most vulnerable to unpredictable energy sources, which has been confirmed to be the timing. Accordingly, the study proposed a truly self-timed SRAM that is realized based on complete handshaking protocols in the 6T bit-cell regulated by a fully Speed Independent (SI) tim-ing circuitry. The study proved the functionality of the proposed design in real silicon. Finally, the project enhanced other performance metrics of the self-timed SRAM concentrating on the bit-line length and the minimum operational voltage by employing several additional design techniques.Umm Al-Qura University, the Ministry of Higher Education in the Kingdom of Saudi Arabia, and the Saudi Cultural Burea