2,507 research outputs found
A Robust Self-calibrating Transmission Scheme for On-Chip Networks
Systems-on-Chip (SoC) design involves several challenges, stemming from the extreme miniaturization of the physical features and from the large number of devices and wires on a chip. Since most SoCs are used within embedded systems, specific concerns are increasingly related to correct, reliable, and robust operation. We believe that in the future most SoCs will be assembled by using large-scale macro-cells and interconnected by means of on-chip networks. We examine some physical properties of on-chip interconnect busses, with the goal of achieving fast, reliable, and low-energy communication. These objectives are reached by dynamically scaling down the voltage swing, while ensuring data integrity-in spite of the decreased signal to noise ratio-by means of encoding and retransmission schemes. In particular, we describe a closed-loop voltage swing controller that samples the error retransmission rate to determine the operational voltage swing. We present a control policy which achieves our goals with minimal complexity; such simplicity is demonstrated by implementing the policy in a synthesizable controller. Such a controller is an embodiment of a self-calibrating circuit that compensates for significant manufacturing parameter deviations and environmental variations. Experimental results show that energy savings amount up to 42%, while at the same time meeting performance requirements
Low-swing signaling for energy efficient on-chip networks
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 65-69).On-chip networks have emerged as a scalable and high-bandwidth communication fabric in many-core processor chips. However, the energy consumption of these networks is becoming comparable to that of computation cores, making further scaling of core counts difficult. This thesis makes several contributions to low-swing signaling circuit design for the energy efficient on-chip networks in two separate projects: on-chip networks optimized for one-to-many multicasts and broadcasts, and link designs that allow on-chip networks to approach an ideal interconnection fabric. A low-swing crossbar switch, which is based on tri-state Reduced-Swing Drivers (RSDs), is presented for the first project. Measurement results of its test chip fabricated in 45nm SOI CMOS show that the tri-state RSD-based crossbar enables 55% power savings as compared to an equivalent full-swing crossbar and link. Also, the measurement results show that the proposed crossbar allows the broadcast-optimized on-chip networks using a single pipeline stage for physical data transmission to operate at 21% higher data rate, when compared with the full-swing networks. For the second project, two clockless low-swing repeaters, a Self-Resetting Logic Repeater (SRLR) and a Voltage-Locked Repeater (VLR), have been proposed and analyzed in simulation only. They both require no reference clock, differential signaling, and bias current. Such digital-intensive properties enable them to approach energy and delay performance of a point-to-point interconnect of variable lengths. Simulated in 45nm SOI CMOS, the 10mm SRLR featured with high energy efficiency consumes 338fJ/b at 5.4Gb/s/ch while the 10mm VLR raises its data rate up to 16.OGb/s/ch with 427fJ/b.by Sunghyun Park.S.M
Addressing the programming challenges of practical interferometric mesh based optical processors
We demonstrate a novel mesh of Mach-Zehnder interferometers (MZIs) for
programmable optical processors. The proposed mesh, referred to as Bokun mesh,
is an architecture that merges the attributes of the prior topologies Diamond
and Clements. Similar to Diamond, Bokun provides diagonal paths passing through
every individual MZI enabling direct phase monitoring. However, unlike Diamond
and similar to Clements, Bokun maintains a minimum optical depth leading to
better scalability. Providing the monitoring option, Bokun's programming is
faster improving the total energy efficiency of the processor. The performance
of Bokun mesh enabled by an optimal optical depth is also more resilient to the
loss and fabrication imperfections compared to architectures with longer depth
such as Reck and Diamond. Employing an efficient programming scheme, the
proposed architecture improves energy efficiency by 83% maintaining the same
computation accuracy for weight matrix changes at 2 kHz
Anchor Self-Calibrating Schemes for UWB based Indoor Localization
Traditional indoor localization techniques that use Received Signal Strength or Inertial Measurement Units for dead-reckoning suffer from signal attenuation and sensor drift, resulting in inaccurate position estimates. Newly available Ultra-Wideband radio modules can measure distances at a centimeter-level accuracy while mitigating the effects of multipath propagation due to their very fine time resolution. Known locations of fixed anchor nodes are required to determine the position of tag nodes within an indoor environment. For a large system consisting of several anchor nodes spanning a wide area, physically mapping out the locations of each anchor node is a tedious task and thus makes the scalability of such systems difficult. Hence it is important to develop indoor localization systems wherein the anchors can self-calibrate by determining their relative positions in Euclidean 3D space with respect to each other. In this thesis, we propose two novel anchor self-calibrating algorithms - Triangle Reconstruction Algorithm (TRA) and Channel Impulse Response Positioning (CIRPos) that improve upon existing range-based implementations and solve existing problems such as flip ambiguity and node localization success rate. The localization accuracy and scalability of the self-calibrating anchor schemes are tested in a simulated environment based on the ranging accuracy of the Ultra-Wideband modules
On Real-Time AER 2-D Convolutions Hardware for Neuromorphic Spike-Based Cortical Processing
In this paper, a chip that performs real-time image
convolutions with programmable kernels of arbitrary shape is presented.
The chip is a first experimental prototype of reduced size
to validate the implemented circuits and system level techniques.
The convolution processing is based on the addressâevent-representation
(AER) technique, which is a spike-based biologically
inspired image and video representation technique that favors
communication bandwidth for pixels with more information. As
a first test prototype, a pixel array of 16x16 has been implemented
with programmable kernel size of up to 16x16. The
chip has been fabricated in a standard 0.35- m complimentary
metalâoxideâsemiconductor (CMOS) process. The technique also
allows to process larger size images by assembling 2-D arrays of
such chips. Pixel operation exploits low-power mixed analogâdigital
circuit techniques. Because of the low currents involved (down
to nanoamperes or even picoamperes), an important amount of
pixel area is devoted to mismatch calibration. The rest of the
chip uses digital circuit techniques, both synchronous and asynchronous.
The fabricated chip has been thoroughly tested, both at
the pixel level and at the system level. Specific computer interfaces
have been developed for generating AER streams from conventional
computers and feeding them as inputs to the convolution
chip, and for grabbing AER streams coming out of the convolution
chip and storing and analyzing them on computers. Extensive
experimental results are provided. At the end of this paper, we
provide discussions and results on scaling up the approach for
larger pixel arrays and multilayer cortical AER systems.Commission of the European Communities IST-2001-34124 (CAVIAR)Commission of the European Communities 216777 (NABAB)Ministerio de EducaciĂłn y Ciencia TIC-2000-0406-P4Ministerio de EducaciĂłn y Ciencia TIC-2003-08164-C03-01Ministerio de EducaciĂłn y Ciencia TEC2006-11730-C03-01Junta de AndalucĂa TIC-141
Designing Robust Systems with Uncertain Information
Considerable sharing of disease alleles among populations is well-characterized in autoimmune disorders (e.g., rheumatoid arthritis), but there are some exceptional loci showing heterogenic association among populations. Here we investigated genetic variants with distinct effects on the development of rheumatoid arthritis in Asian and European populations. Ancestry-related association heterogeneity was examined using Cochran's homogeneity tests for the disease association data from large Asian (n = 14,465; 9,299 discovery subjects and 5,166 validation subjects; 4 collections) and European (n = 45,790; 11 collections) rheumatoid arthritis case-control cohorts with Immunochip and genome-wide SNP array data. We identified significant heterogeneity between the two ancestries for the common variants in the GTF2I locus (P-Heterogeneity = 9.6 x 10(-9) at rs73366469) and showed that this heterogeneity was due to an Asian-specific association effect (ORMeta = 1.37 and P-Meta = 4.2 x 10(-13) in Asians; ORMeta = 1.00 and P-Meta = 1.00 in Europeans). Trans-ancestral comparison and bioinfomatics analysis revealed a plausibly causal or disease-variant-tagging SNP (rs117026326; in linkage disequilibrium with rs73366469), whose minor allele is common in Asians but rare in Europeans. In conclusion, we identified largest-ever effect on Asian rheumatoid arthritis across human non-HLA regions at GTF2I by heterogeneity mapping followed by replication studies, and pinpointed a possible causal variant.We are grateful to all study participants and those who previously contributed to the European association statistics. This study was supported by the Korea Healthcare Technology R&D Project of the Ministry for Health & Welfare (HI13C2124), the Japanese Ministry of Education, Culture, Sports, Science and Technology Grant-in-Aid for Scientific Research (15H04965) and the US National Institutes of Health (R01MD007909 and R01AR060366)
Design of variability compensation architectures of digital circuits with adaptive body bias
The most critical concern in circuit is to achieve high level of performance with very tight power constraint. As the high performance circuits moved beyond 45nm technology one of the major issues is the parameter variation i.e. deviation in process, temperature and voltage (PVT) values from nominal specifications. A key process parameter subject to variation is the transistor threshold voltage (Vth) which impacts two important parameters: frequency and leakage power. Although the degradation can be compensated by the worstcase scenario based over-design approach, it induces remarkable power and performance overhead which is undesirable in tightly constrained designs. Dynamic voltage scaling (DVS) is a more power efficient approach, however its coarse granularity implies difficulty in handling fine grained variations. These factors have contributed to the growing interest in power aware robust circuit design. We propose a variability compensation architecture with adaptive body bias, for low power applications using 28nm FDSOI technology. The basic approach is based on a dynamic prediction and prevention of possible circuit timing errors. In our proposal we are using a Canary logic technique that enables the typical-case design. The body bias generation is based on a DLL type method which uses an external reference generator and voltage controlled delay line (VCDL) to generate the forward body bias (FBB) control signals. The adaptive technique is used for dynamic detection and correction of path failures in digital designs due to PVT variations. Instead of tuning the supply voltage, the key idea of the design approach is to tune the body bias voltage bymonitoring the error rate during operation. The FBB increases operating speed with an overhead in leakage power
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
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