A Context-Switching/Dual-Context ROM Augmented RAM using Standard 8T SRAM

The landscape of emerging applications has been continually widening, encompassing various data-intensive applications like artificial intelligence, machine learning, secure encryption, Internet-of-Things, etc. A sustainable approach toward creating dedicated hardware platforms that can cater to multiple applications often requires the underlying hardware to context-switch or support more than one context simultaneously. This paper presents a context-switching and dual-context memory based on the standard 8T SRAM bit-cell. Specifically, we exploit the availability of multi-VT transistors by selectively choosing the read-port transistors of the 8T SRAM cell to be either high-VT or low-VT. The 8T SRAM cell is thus augmented to store ROM data (represented as the VT of the transistors constituting the read-port) while simultaneously storing RAM data. Further, we propose specific sensing methodologies such that the memory array can support RAM-only or ROM-only mode (context-switching (CS) mode) or RAM and ROM mode simultaneously (dual-context (DC) mode). Extensive Monte-Carlo simulations have verified the robustness of our proposed ROM-augmented CS/DC memory on the Globalfoundries 22nm-FDX technology node

Improving Phase Change Memory (PCM) and Spin-Torque-Transfer Magnetic-RAM (STT-MRAM) as Next-Generation Memories: A Circuit Perspective

In the memory hierarchy of computer systems, the traditional semiconductor memories Static RAM (SRAM) and Dynamic RAM (DRAM) have already served for several decades as cache and main memory. With technology scaling, they face increasingly intractable challenges like power, density, reliability and scalability. As a result, they become less appealing in the multi/many-core era with ever increasing size and memory-intensity of working sets. Recently, there is an increasing interest in using emerging non-volatile memory technologies in replacement of SRAM and DRAM, due to their advantages like non-volatility, high device density, near-zero cell leakage and resilience to soft errors. Among several new memory technologies, Phase Change Memory (PCM) and Spin-Torque-Transfer Magnetic-RAM (STT-MRAM) are most promising candidates in building main memory and cache, respectively. However, both of them possess unique limitations that preventing them from being effectively adopted. In this dissertation, I present my circuit design work on tackling the limitations of PCM and STT-MRAM. At bit level, both PCM and STT-MRAM suffer from excessive write energy, and PCM has very limited write endurance. For PCM, I implement Differential Write to remove large number of unnecessary bit-writes that do not alter the stored data. It is then extended to STT-MRAM as Early Write Termination, with specific optimizations to eliminate the overhead of pre-write read. At array level, PCM enjoys high density but could not provide competitive throughput due to its long write latency and limited number of read/write circuits. I propose a Pseudo-Multi-Port Bank design to exploit intra-bank parallelism by recycling and reusing shared peripheral circuits between accesses in a time-multiplexed manner. On the other hand, although STT-MRAM features satisfactory throughput, its conventional array architecture is constrained on density and scalability by the pitch of the per-column bitline pair. I propose a Common-Source-Line Array architecture which uses a shared source-line along the row, essentially leaving only one bitline per column. For these techniques, I provide circuit level analyses as well as architecture/system level and/or process/device level discussions. In addition, relevant background and work are thoroughly surveyed and potential future research topics are discussed, offering insights and prospects of these next-generation memories

Release and Verification of an Operating System for Testing e-Flash on Microcontrollers for Automotive Applications based on Multicore Architecture

The cars produced contain an increasing number of electronic devices for active assistance to driving, safety controls, energy efficiency, passenger comfort and entertainment. Safety is the keyword and means to have electronic components high reliability. Infineon microcontroller division works to improve reliability and guarantee the quality of microcontroller flash memories. The thesis goal is to verify the operating system used to test the microcontrollers flash memorie

Understanding and Improving the Latency of DRAM-Based Memory Systems

Over the past two decades, the storage capacity and access bandwidth of main memory have improved tremendously, by 128x and 20x, respectively. These improvements are mainly due to the continuous technology scaling of DRAM (dynamic random-access memory), which has been used as the physical substrate for main memory. In stark contrast with capacity and bandwidth, DRAM latency has remained almost constant, reducing by only 1.3x in the same time frame. Therefore, long DRAM latency continues to be a critical performance bottleneck in modern systems. Increasing core counts, and the emergence of increasingly more data-intensive and latency-critical applications further stress the importance of providing low-latency memory access. In this dissertation, we identify three main problems that contribute significantly to long latency of DRAM accesses. To address these problems, we present a series of new techniques. Our new techniques significantly improve both system performance and energy efficiency. We also examine the critical relationship between supply voltage and latency in modern DRAM chips and develop new mechanisms that exploit this voltage-latency trade-off to improve energy efficiency. The key conclusion of this dissertation is that augmenting DRAM architecture with simple and low-cost features, and developing a better understanding of manufactured DRAM chips together lead to significant memory latency reduction as well as energy efficiency improvement. We hope and believe that the proposed architectural techniques and the detailed experimental data and observations on real commodity DRAM chips presented in this dissertation will enable development of other new mechanisms to improve the performance, energy efficiency, or reliability of future memory systems.Comment: PhD Dissertatio

An ultra-low voltage FFT processor using energy-aware techniques

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2004.Page 170 blank.Includes bibliographical references (p. 165-169).In a number of emerging applications such as wireless sensor networks, system lifetime depends on the energy efficiency of computation and communication. The key metric in such applications is the energy dissipated per function rather than traditional ones such as clock speed or silicon area. Hardware designs are shifting focus toward enabling energy-awareness, allowing the processor to be energy-efficient for a variety of operating scenarios. This is in contrast to conventional low-power design, which optimizes for the worst-case scenario. Here, three energy-quality scalable hooks are designed into a real-valued FFT processor: variable FFT length (N=128 to 1024 points), variable bit precision (8,16 bit), and variable voltage supply with variable clock frequency (VDD=1 80mV to 0.9V, and f=164Hz to 6MHz). A variable-bit-precision and variable-FFT-length scalable FFT ASIC using an off-the-shelf standard-cell logic library and memory only scales down to 1V operation. Further energy savings is achieved through ultra-low voltage-supply operation. As performance requirements are relaxed, the operating voltage supply is scaled down, possibly even below the threshold voltage into the subthreshold region. When lower frequencies cause leakage energy dissipation to exceed the active energy dissipation, there is an optimal operating point for minimizing energy consumption.(cont.) Logic and memory design techniques allowing ultra-low voltage operation are employed to study the optimal frequency/voltage operating point for the FFT. A full-custom implementation with circuit techniques optimized for deep voltage scaling into the subthreshold regime, is fabricated using a standard CMOS 0.18[mu]m logic process and functions down to 180mV. At the optimal operating point where the voltage supply is 350mV, the FFT processor dissipates 155nJ/FFT. The custom FFT is 8x more energy-efficient than the ASIC implementation and 350x more energy-efficient than a low-power microprocessor implementation.by Alice Wang.Ph.D

Energy efficient core designs for upcoming process technologies

Energy efficiency has been a first order constraint in the design of micro processors for the last decade. As Moore's law sunsets, new technologies are being actively explored to extend the march in increasing the computational power and efficiency. It is essential for computer architects to understand the opportunities and challenges in utilizing the upcoming process technology trends in order to design the most efficient processors. In this work, we consider three process technology trends and propose core designs that are best suited for each of the technologies. The process technologies are expected to be viable over a span of timelines. We first consider the most popular method currently available to improve the energy efficiency, i.e. by lowering the operating voltage. We make key observations regarding the limiting factors in scaling down the operating voltage for general purpose high performance processors. Later, we propose our novel core design, ScalCore, one that can work in high performance mode at nominal Vdd, and in a very energy-efficient mode at low Vdd. The resulting core design can operate at much lower voltages providing higher parallel performance while consuming lower energy. While lowering Vdd improves the energy efficiency, CMOS devices are fundamentally limited in their low voltage operation. Therefore, we next consider an upcoming device technology -- Tunneling Field-Effect Transistors (TFETs), that is expected to supplement CMOS device technology in the near future. TFETs can attain much higher energy efficiency than CMOS at low voltages. However, their performance saturates at high voltages and, therefore, cannot entirely replace CMOS when high performance is needed. Ideally, we desire a core that is as energy-efficient as TFET and provides as much performance as CMOS. To reach this goal, we characterize the TFET device behavior for core design and judiciously integrate TFET units, CMOS units in a single core. The resulting core, called HetCore, can provide very high energy efficiency while limiting the slowdown when compared to a CMOS core. Finally, we analyze Monolithic 3D (M3D) integration technology that is widely considered to be the only way to integrate more transistors on a chip. We present the first analysis of the architectural implications of using M3D for core design and show how to partition the core across different layers. We also address one of the key challenges in realizing the technology, namely, the top layer performance degradation. We propose a critical path based partitioning for logic stages and asymmetric bit/port partitioning for storage stages. The result is a core that performs nearly as well as a core without any top layer slowdown. When compared to a 2D baseline design, an M3D core not only provides much higher performance, it also reduces the energy consumption at the same time. In summary, this thesis addresses one of the fundamental challenges in computer architecture -- overcoming the fact that CMOS is not scaling anymore. As we increase the computing power on a single chip, our ability to power the entire chip keeps decreasing. This thesis proposes three solutions aimed at solving this problem over different timelines. Across all our solutions, we improve energy efficiency without compromising the performance of the core. As a result, we are able to operate twice as many cores with in the same power budget as regular cores, significantly alleviating the problem of dark silicon

ENERGY-EFFICIENT FEATURE EXTRACTION ENGINE AND SECURE CHIP IDENTIFICATION FOR UBIQUITOUS SURVEILLANCE

Ph.DDOCTOR OF PHILOSOPH

Performance Analysis of NAND Flash Memory Solid-State Disks

As their prices decline, their storage capacities increase, and their endurance improves, NAND Flash Solid-State Disks (SSD) provide an increasingly attractive alternative to Hard Disk Drives (HDD) for portable computing systems and PCs. HDDs have been an integral component of computing systems for several decades as long-term, non-volatile storage in memory hierarchy. Today's typical hard disk drive is a highly complex electro-mechanical system which is a result of decades of research, development, and fine-tuned engineering. Compared to HDD, flash memory provides a simpler interface, one without the complexities of mechanical parts. On the other hand, today's typical solid-state disk drive is still a complex storage system with its own peculiarities and system problems. Due to lack of publicly available SSD models, we have developed our NAND flash SSD models and integrated them into DiskSim, which is extensively used in academe in studying storage system architectures. With our flash memory simulator, we model various solid-state disk architectures for a typical portable computing environment, quantify their performance under real user PC workloads and explore potential for further improvements. We find the following: * The real limitation to NAND flash memory performance is not its low per-device bandwidth but its internal core interface. * NAND flash memory media transfer rates do not need to scale up to those of HDDs for good performance. * SSD organizations that exploit concurrency at both the system and device level improve performance significantly. * These system- and device-level concurrency mechanisms are, to a significant degree, orthogonal: that is, the performance increase due to one does not come at the expense of the other, as each exploits a different facet of concurrency exhibited within the PC workload. * SSD performance can be further improved by implementing flash-oriented queuing algorithms, access reordering, and bus ordering algorithms which exploit the flash memory interface and its timing differences between read and write requests

Semiconductor Memory Applications in Radiation Environment, Hardware Security and Machine Learning System

abstract: Semiconductor memory is a key component of the computing systems. Beyond the conventional memory and data storage applications, in this dissertation, both mainstream and eNVM memory technologies are explored for radiation environment, hardware security system and machine learning applications. In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, single event effects of the oxide based resistive switching random memory (RRAM), one of eNVM technologies, is investigated from the circuit-level to the system level. Physical Unclonable Function (PUF) has been widely investigated as a promising hardware security primitive, which employs the inherent randomness in a physical system (e.g. the intrinsic semiconductor manufacturing variability). In the dissertation, two RRAM-based PUF implementations are proposed for cryptographic key generation (weak PUF) and device authentication (strong PUF), respectively. The performance of the RRAM PUFs are evaluated with experiment and simulation. The impact of non-ideal circuit effects on the performance of the PUFs is also investigated and optimization strategies are proposed to solve the non-ideal effects. Besides, the security resistance against modeling and machine learning attacks is analyzed as well. Deep neural networks (DNNs) have shown remarkable improvements in various intelligent applications such as image classification, speech classification and object localization and detection. Increasing efforts have been devoted to develop hardware accelerators. In this dissertation, two types of compute-in-memory (CIM) based hardware accelerator designs with SRAM and eNVM technologies are proposed for two binary neural networks, i.e. hybrid BNN (HBNN) and XNOR-BNN, respectively, which are explored for the hardware resource-limited platforms, e.g. edge devices.. These designs feature with high the throughput, scalability, low latency and high energy efficiency. Finally, we have successfully taped-out and validated the proposed designs with SRAM technology in TSMC 65 nm. Overall, this dissertation paves the paths for memory technologies’ new applications towards the secure and energy-efficient artificial intelligence system.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201