63 research outputs found
Reliable and Energy Efficient MLC STT-RAM Buffer for CNN Accelerators
We propose a lightweight scheme where the formation of a data block is changed in such a way that it can tolerate soft errors significantly better than the baseline. The key insight behind our work is that CNN weights are normalized between -1 and 1 after each convolutional layer, and this leaves one bit unused in half-precision floating-point representation. By taking advantage of the unused bit, we create a backup for the most significant bit to protect it against the soft errors. Also, considering the fact that in MLC STT-RAMs the cost of memory operations (read and write), and reliability of a cell are content-dependent (some patterns take larger current and longer time, while they are more susceptible to soft error), we rearrange the data block to minimize the number of costly bit patterns. Combining these two techniques provides the same level of accuracy compared to an error-free baseline while improving the read and write energy by 9% and 6%, respectively
Towards Energy-Efficient and Reliable Computing: From Highly-Scaled CMOS Devices to Resistive Memories
The continuous increase in transistor density based on Moore\u27s Law has led us to highly scaled Complementary Metal-Oxide Semiconductor (CMOS) technologies. These transistor-based process technologies offer improved density as well as a reduction in nominal supply voltage. An analysis regarding different aspects of 45nm and 15nm technologies, such as power consumption and cell area to compare these two technologies is proposed on an IEEE 754 Single Precision Floating-Point Unit implementation. Based on the results, using the 15nm technology offers 4-times less energy and 3-fold smaller footprint. New challenges also arise, such as relative proportion of leakage power in standby mode that can be addressed by post-CMOS technologies. Spin-Transfer Torque Random Access Memory (STT-MRAM) has been explored as a post-CMOS technology for embedded and data storage applications seeking non-volatility, near-zero standby energy, and high density. Towards attaining these objectives for practical implementations, various techniques to mitigate the specific reliability challenges associated with STT-MRAM elements are surveyed, classified, and assessed herein. Cost and suitability metrics assessed include the area of nanomagmetic and CMOS components per bit, access time and complexity, Sense Margin (SM), and energy or power consumption costs versus resiliency benefits. In an attempt to further improve the Process Variation (PV) immunity of the Sense Amplifiers (SAs), a new SA has been introduced called Adaptive Sense Amplifier (ASA). ASA can benefit from low Bit Error Rate (BER) and low Energy Delay Product (EDP) by combining the properties of two of the commonly used SAs, Pre-Charge Sense Amplifier (PCSA) and Separated Pre-Charge Sense Amplifier (SPCSA). ASA can operate in either PCSA or SPCSA mode based on the requirements of the circuit such as energy efficiency or reliability. Then, ASA is utilized to propose a novel approach to actually leverage the PV in Non-Volatile Memory (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
STT-MRAM characterization and its test implications
Spin torque transfer (STT)-magnetoresistive random-access memory (MRAM) has come
a long way in research to meet the speed and power consumption requirements for future
memory applications. The state-of-the-art STT-MRAM bit-cells employ magnetic tunnel
junction (MTJ) with perpendicular magnetic anisotropy (PMA). The process repeatabil-
ity and yield stability for wafer fabrication are some of the critical issues encountered in
STT-MRAM mass production. Some of the yield improvement techniques to combat the
e ect of process variations have been previously explored. However, little research has been
done on defect oriented testing of STT-MRAM arrays. In this thesis, the author investi-
gates the parameter deviation and non-idealities encountered during the development of
a novel MTJ stack con guration. The characterization result provides motivation for the
development of the design for testability (DFT) scheme that can help test and characterize
STT-MRAM bit-cells and the CMOS peripheral circuitry e ciently.
The primary factors for wafer yield degradation are the device parameter variation and
its non-uniformity across the wafer due to the fabrication process non-idealities. There-
fore, e ective in-process testing strategies for exploring and verifying the impact of the
parameter variation on the wafer yield will be needed to achieve fabrication process opti-
mization. While yield depends on the CMOS process variability, quality of the deposited
MTJ lm, and other process non-idealities, test platform can enable parametric optimiza-
tion and veri cation using the CMOS-based DFT circuits. In this work, we develop a DFT
algorithm and implement a DFT circuit for parametric testing and prequali cation of the
critical circuits in the CMOS wafer. The DFT circuit successfully replicates the electrical
characteristics of MTJ devices and captures their spatial variation across the wafer with
an error of less than 4%. We estimate the yield of the read sensing path by implement-
ing the DFT circuit, which can replicate the resistance-area product variation up to 50%
from its nominal value. The yield data from the read sensing path at di erent wafer loca-
tions are analyzed, and a usable wafer radius has been estimated. Our DFT scheme can
provide quantitative feedback based on in-die measurement, enabling fabrication process
optimization through iterative estimation and veri cation of the calibrated parameters.
Another concern that prevents mass production of STT-MRAM arrays is the defect
formation in MTJ devices due to aging. Identifying manufacturing defects in the magnetic
tunnel junction (MTJ) device is crucial for the yield and reliability of spin-torque-transfer
(STT) magnetic random-access memory (MRAM) arrays. Several of the MTJ defects result
in parametric deviations of the device that deteriorate over time. We extend our work on
the DFT scheme by monitoring the electrical parameter deviations occurring due to the
defect formation over time. A programmable DFT scheme was implemented for a sub-arrayin 65 nm CMOS technology to evaluate the feasibility of the test scheme. The scheme utilizes the read sense path to compare the bit-cell electrical parameters against known
DFT cells characteristics. Built-in-self-test (BIST) methodology is utilized to trigger the
onset of the fault once the device parameter crosses a threshold value. We demonstrate
the operation and evaluate the accuracy of detection with the proposed scheme. The
DFT scheme can be exploited for monitoring aging defects, modeling their behavior and
optimization of the fabrication process.
DFT scheme could potentially nd numerous applications for parametric characteriza-
tion and fault monitoring of STT-MRAM bit-cell arrays during mass production. Some of
the applications include a) Fabrication process feedback to improve wafer turnaround time,
b) STT-MRAM bit-cell health monitoring, c) Decoupled characterization of the CMOS pe-
ripheral circuitry such as read-sensing path and sense ampli er characterization within the
STT-MRAM array. Additionally, the DFT scheme has potential applications for detec-
tion of fault formation that could be utilized for deploying redundancy schemes, providing
a graceful degradation in MTJ-based bit-cell array due to aging of the device, and also
providing feedback to improve the fabrication process and yield learning
Bio-inspired learning and hardware acceleration with emerging memories
Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called action potentials. Motivated by these facts, the Spiking Neural Networks (SNNs), which are a bio-plausible version of neural networks have been proposed as an alternative computing paradigm where the timing of spikes generated by artificial neurons is central to its learning and inference capabilities. This dissertation demonstrates the computational power of the SNNs using conventional CMOS and emerging nanoscale hardware platforms.
The first half of this dissertation presents an SNN architecture which is trained using a supervised spike-based learning algorithm for the handwritten digit classification problem. This network achieves an accuracy of 98.17% on the MNIST test data-set, with about 4X fewer parameters compared to the state-of-the-art neural networks achieving over 99% accuracy. In addition, a scheme for parallelizing and speeding up the SNN simulation on a GPU platform is presented. The second half of this dissertation presents an optimal hardware design for accelerating SNN inference and training with SRAM (Static Random Access Memory) and nanoscale non-volatile memory (NVM) crossbar arrays. Three prominent NVM devices are studied for realizing hardware accelerators for SNNs: Phase Change Memory (PCM), Spin Transfer Torque RAM (STT-RAM) and Resistive RAM (RRAM). The analysis shows that a spike-based inference engine with crossbar arrays of STT-RAM bit-cells is 2X and 5X more efficient compared to PCM and RRAM memories, respectively. Furthermore, the STT-RAM design has nearly 6X higher throughput per unit Watt per unit area than that of an equivalent SRAM-based (Static Random Access Memory) design. A hardware accelerator with on-chip learning on an STT-RAM memory array is also designed, requiring bits of floating-point synaptic weight precision to reach the baseline SNN algorithmic performance on the MNIST dataset. The complete design with STT-RAM crossbar array achieves nearly 20X higher throughput per unit Watt per unit mm^2 than an equivalent design with SRAM memory.
In summary, this work demonstrates the potential of spike-based neuromorphic computing algorithms and its efficient realization in hardware based on conventional CMOS as well as emerging technologies. The schemes presented here can be further extended to design spike-based systems that can be ubiquitously deployed for energy and memory constrained edge computing applications
X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories
Silicon-based Static Random Access Memories (SRAM) and digital Boolean logic
have been the workhorse of the state-of-art computing platforms. Despite
tremendous strides in scaling the ubiquitous metal-oxide-semiconductor
transistor, the underlying \textit{von-Neumann} computing architecture has
remained unchanged. The limited throughput and energy-efficiency of the
state-of-art computing systems, to a large extent, results from the well-known
\textit{von-Neumann bottleneck}. The energy and throughput inefficiency of the
von-Neumann machines have been accentuated in recent times due to the present
emphasis on data-intensive applications like artificial intelligence, machine
learning \textit{etc}. A possible approach towards mitigating the overhead
associated with the von-Neumann bottleneck is to enable \textit{in-memory}
Boolean computations. In this manuscript, we present an augmented version of
the conventional SRAM bit-cells, called \textit{the X-SRAM}, with the ability
to perform in-memory, vector Boolean computations, in addition to the usual
memory storage operations. We propose at least six different schemes for
enabling in-memory vector computations including NAND, NOR, IMP (implication),
XOR logic gates with respect to different bit-cell topologies the 8T cell
and the 8T Differential cell. In addition, we also present a novel
\textit{`read-compute-store'} scheme, wherein the computed Boolean function can
be directly stored in the memory without the need of latching the data and
carrying out a subsequent write operation. The feasibility of the proposed
schemes has been verified using predictive transistor models and Monte-Carlo
variation analysis.Comment: This article has been accepted in a future issue of IEEE Transactions
on Circuits and Systems-I: Regular Paper
Circuit and Architecture Co-Design of STT-RAM for High Performance and Low Energy
Spin-Transfer Torque Random Access Memory (STT-RAM) has been proved a promising emerging nonvolatile memory technology suitable for many applications such as cache mem- ory of CPU. Compared with other conventional memory technology, STT-RAM offers many attractive features such as nonvolatility, fast random access speed and extreme low leakage power.
However, STT-RAM is still facing many challenges. First of all, programming STT-RAM is a stochastic process due to random thermal fluctuations, so the write errors are hard to avoid. Secondly, the existing STT-RAM cell designs can be used for only single-port accesses, which limits the memory access bandwidth and constraints the system performance. Finally, while other memory technology supports multi-level cell (MLC) design to boost the storage density, adopting MLC to STT-RAM brings many disadvantages such as requirement for large transistor and low access speed. In this work, we proposed solutions on both circuit and architecture level to address these challenges.
For the write error issues, we proposed two probabilistic methods, namely write-verify- rewrite with adaptive period (WRAP) and verify-one-while-writing (VOW), for performance improvement and write failure reduction.
For dual-port solution, we propose the design methods to support dual-port accesses for STT-RAM. The area increment by introducing an additional port is reduced by leveraging the shared source-line structure. Detailed analysis on the performance/reliability degrada- tion caused by dual-port accesses is performed, and the corresponding design optimization is provided.
To unleash the potential of MLC STT-RAM cache, we proposed a new design through a cross-layer co-optimization. The memory cell structure integrated the reversed stacking of magnetic junction tunneling (MTJ) for a more balanced device and design trade-off. In architecture development, we presented an adaptive mode switching mechanism: based on application’s memory access behavior, the MLC STT-RAM cache can dynamically change between low latency SLC mode and high capacity MLC mode.
Finally, we present a 4Kb test chip design which can support different types and sizes of MTJs. A configurable sensing solution is used in the test chip so that it can support wide range of MTJ resistance. Such test chip design can help to evaluate various type of MTJs in the future
LOW POWER CIRCUITS DESIGN USING RESISTIVE NON-VOLATILE MEMORIES
Ph.DDOCTOR OF PHILOSOPH
Reliable Low-Power High Performance Spintronic Memories
Moores Gesetz folgend, ist es der Chipindustrie in den letzten fünf Jahrzehnten gelungen, ein
explosionsartiges Wachstum zu erreichen. Dies hatte ebenso einen exponentiellen Anstieg der
Nachfrage von Speicherkomponenten zur Folge, was wiederum zu speicherlastigen Chips in
den heutigen Computersystemen führt. Allerdings stellen traditionelle on-Chip Speichertech-
nologien wie Static Random Access Memories (SRAMs), Dynamic Random Access Memories
(DRAMs) und Flip-Flops eine Herausforderung in Bezug auf Skalierbarkeit, Verlustleistung
und Zuverlässigkeit dar. Eben jene Herausforderungen und die überwältigende Nachfrage
nach höherer Performanz und Integrationsdichte des on-Chip Speichers motivieren Forscher,
nach neuen nichtflüchtigen Speichertechnologien zu suchen. Aufkommende spintronische Spe-
ichertechnologien wie Spin Orbit Torque (SOT) und Spin Transfer Torque (STT) erhielten
in den letzten Jahren eine hohe Aufmerksamkeit, da sie eine Reihe an Vorteilen bieten. Dazu
gehören Nichtflüchtigkeit, Skalierbarkeit, hohe Beständigkeit, CMOS Kompatibilität und Unan-
fälligkeit gegenüber Soft-Errors. In der Spintronik repräsentiert der Spin eines Elektrons dessen
Information. Das Datum wird durch die Höhe des Widerstandes gespeichert, welche sich durch
das Anlegen eines polarisierten Stroms an das Speichermedium verändern lässt. Das Prob-
lem der statischen Leistung gehen die Speichergeräte sowohl durch deren verlustleistungsfreie
Eigenschaft, als auch durch ihr Standard- Aus/Sofort-Ein Verhalten an. Nichtsdestotrotz sind
noch andere Probleme, wie die hohe Zugriffslatenz und die Energieaufnahme zu lösen, bevor
sie eine verbreitete Anwendung finden können. Um diesen Problemen gerecht zu werden, sind
neue Computerparadigmen, -architekturen und -entwurfsphilosophien notwendig.
Die hohe Zugriffslatenz der Spintroniktechnologie ist auf eine vergleichsweise lange Schalt-
dauer zurückzuführen, welche die von konventionellem SRAM übersteigt. Des Weiteren ist auf
Grund des stochastischen Schaltvorgangs der Speicherzelle und des Einflusses der Prozessvari-
ation ein nicht zu vernachlässigender Zeitraum dafür erforderlich. In diesem Zeitraum wird ein
konstanter Schreibstrom durch die Bitzelle geleitet, um den Schaltvorgang zu gewährleisten.
Dieser Vorgang verursacht eine hohe Energieaufnahme. Für die Leseoperation wird gleicher-
maßen ein beachtliches Zeitfenster benötigt, ebenfalls bedingt durch den Einfluss der Prozess-
variation. Dem gegenüber stehen diverse Zuverlässigkeitsprobleme. Dazu gehören unter An-
derem die Leseintereferenz und andere Degenerationspobleme, wie das des Time Dependent Di-
electric Breakdowns (TDDB). Diese Zuverlässigkeitsprobleme sind wiederum auf die benötigten
längeren Schaltzeiten zurückzuführen, welche in der Folge auch einen über längere Zeit an-
liegenden Lese- bzw. Schreibstrom implizieren. Es ist daher notwendig, sowohl die Energie, als
auch die Latenz zur Steigerung der Zuverlässigkeit zu reduzieren, um daraus einen potenziellen
Kandidaten für ein on-Chip Speichersystem zu machen.
In dieser Dissertation werden wir Entwurfsstrategien vorstellen, welche das Ziel verfolgen,
die Herausforderungen des Cache-, Register- und Flip-Flop-Entwurfs anzugehen. Dies erre-
ichen wir unter Zuhilfenahme eines Cross-Layer Ansatzes. Für Caches entwickelten wir ver-
schiedene Ansätze auf Schaltkreisebene, welche sowohl auf der Speicherarchitekturebene, als
auch auf der Systemebene in Bezug auf Energieaufnahme, Performanzsteigerung und Zuver-
lässigkeitverbesserung evaluiert werden. Wir entwickeln eine Selbstabschalttechnik, sowohl für
die Lese-, als auch die Schreiboperation von Caches. Diese ist in der Lage, den Abschluss der
entsprechenden Operation dynamisch zu ermitteln. Nachdem der Abschluss erkannt wurde,
wird die Lese- bzw. Schreiboperation sofort gestoppt, um Energie zu sparen. Zusätzlich
limitiert die Selbstabschalttechnik die Dauer des Stromflusses durch die Speicherzelle, was
wiederum das Auftreten von TDDB und Leseinterferenz bei Schreib- bzw. Leseoperationen re-
duziert. Zur Verbesserung der Schreiblatenz heben wir den Schreibstrom an der Bitzelle an, um den magnetischen Schaltprozess zu beschleunigen. Um registerbankspezifische Anforderungen
zu berücksichtigen, haben wir zusätzlich eine Multiport-Speicherarchitektur entworfen, welche
eine einzigartige Eigenschaft der SOT-Zelle ausnutzt, um simultan Lese- und Schreiboperatio-
nen auszuführen. Es ist daher möglich Lese/Schreib- Konfilkte auf Bitzellen-Ebene zu lösen,
was sich wiederum in einer sehr viel einfacheren Multiport- Registerbankarchitektur nieder-
schlägt.
Zusätzlich zu den Speicheransätzen haben wir ebenfalls zwei Flip-Flop-Architekturen vorgestellt.
Die erste ist eine nichtflüchtige non-Shadow Flip-Flop-Architektur, welche die Speicherzelle als
aktive Komponente nutzt. Dies ermöglicht das sofortige An- und Ausschalten der Versorgungss-
pannung und ist daher besonders gut für aggressives Powergating geeignet. Alles in Allem zeigt
der vorgestellte Flip-Flop-Entwurf eine ähnliche Timing-Charakteristik wie die konventioneller
CMOS Flip-Flops auf. Jedoch erlaubt er zur selben Zeit eine signifikante Reduktion der statis-
chen Leistungsaufnahme im Vergleich zu nichtflüchtigen Shadow- Flip-Flops. Die zweite ist eine
fehlertolerante Flip-Flop-Architektur, welche sich unanfällig gegenüber diversen Defekten und
Fehlern verhält. Die Leistungsfähigkeit aller vorgestellten Techniken wird durch ausführliche
Simulationen auf Schaltkreisebene verdeutlicht, welche weiter durch detaillierte Evaluationen
auf Systemebene untermauert werden. Im Allgemeinen konnten wir verschiedene Techniken en-
twickeln, die erhebliche Verbesserungen in Bezug auf Performanz, Energie und Zuverlässigkeit
von spintronischen on-Chip Speichern, wie Caches, Register und Flip-Flops erreichen
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