2,629 research outputs found
TrainWare
No full textTraining convolutional neural network on device has become essential where it allows applications to consider user's individual environment. Meanwhile, the weight update operation from the training process is the primary factor of high energy consumption due to its substantial memory accesses. We propose a dedicated weight update architecture with two key features: (1) a specialized local buffer for the DRAM access deduction (2) a novel dataflow and its suitable processing element array structure for weight gradient computation to optimize the energy consumed by internal memories. Our scheme achieves 14.3%-30.2% total energy reduction by drastically eliminating the memory accesses
A Picosecond-Resolution Digitally-Controlled Timing Generator with One-Clock-Latency at Arbitrary Instantaneous Input
No full text1
PowerField
No full textTransient temperature-to-power conversion is as important as steady-state analysis since power distributions tend to change dynamically. In this work, we propose PowerField framework to find the most probable power distribution from consecutive thermal images. Since the transient analysis is vulnerable to spatio-temporal thermal noise, we adopted a maximum-a-posteriori Markov random field framework to enhance the noise immunity. The most probable power map is obtained by minimizing the energy function which is calculated using an approximated transient thermal equation. Experimental results with a thermal simulator shows that PowerField outperforms the previous method in transient analysis reducing the error by half on average. We also applied our method to a real silicon achieving 90.7% accuracy
A pattern-dependent injection-locked CDR for clock-embedded signaling
No full textThis paper presents a CDR architecture for clock-embedded signaling. To suppress the effect of data-dependent jitter of the conventional DLL-based approach, we propose a pattern-dependent injection-locking scheme in a PLL-based clock recovery circuit. It achieves both benefits of PLL and DLL, the input jitter filtering and the clearance of accumulated VCO jitter, respectively. A jitter analysis is also presented to develop a design strategy for the optimal extraction of injection timing from random data stream. The CDR, implemented in a 28 nm CMOS, achieves a data rate of 12.5 Gb/s with a 13.7 dB-loss channel and verifies the validity of the analysis.11Nsciescopu
A 250μW 2.4GHz Fast-Lock Fractional-N Frequency Generation for Ultra-Low-Power Applications
No full textThis brief presents a fast-lock 2.4-GHz fractional-N phase-locked loop (PLL) for ultralow-power applications. To minimize the power consumed by all the other circuits except for the main oscillator, we propose a master-slave PLL structure in which a low-frequency master PLL is followed by a slave injection-locked oscillator operating at high frequency. A frequency-error compensation circuit is also implemented in the slave oscillator to eliminate possible drift in the free-running frequency. With a fractional-N coarse-lock unit in the master PLL and a fine frequency initialization unit in the slave oscillator, the PLL supports two fast-lock modes: 1) start-up locking from deep-power-down mode and 2) instantaneous relocking from standby mode. The implemented PLL in 65-nm complementary metal-oxide-semiconductor (CMOS) consumes 250 μW from a 0.8-V supply, demonstrating a power efficiency of 0.102 mW/GHz. The PLL performs the two fast-lock operations with lock times of less than 22 μs from deep power down and 1 μs from standby, respectively.112sciescopu
Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning
No full textThis article presents an efficient learning-based method to solve the <italic>inverse kinematic</italic> (IK) problem on soft robots with highly nonlinear deformation. The major challenge of efficiently computing IK for such robots is due to the lack of analytical formulation for either forward or inverse kinematics. To address this challenge, we employ neural networks to learn both the mapping function of forward kinematics and also the Jacobian of this function. As a result, Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real training transfer strategy is conducted to make this approach more practical. We first generate a large number of samples in a simulation environment for learning both the kinematic and the Jacobian networks of a soft robot design. Thereafter, a sim-to-real layer of differentiable neurons is employed to map the results of simulation to the physical hardware, where this sim-to-real layer can be learned from a very limited number of training samples generated on the hardware.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Materials and ManufacturingMechatronic Desig
에너지 효율적인 심층 컨볼루셔널 신경망 프로세서 및 DRAM 내부 연산 프레임워크
No full text학위논문(박사) - 한국과학기술원 : 전기및전자공학부, 2019.2,[vii, 84 p. :]Recent deep convolutional neural networks (CNNs) are outperforming conventional hand-crafted algorithms in a wide variety of intelligent vision tasks, but they require billons of operations and hundreds million of weights. To process large-scale CNNs energy-efficiently, three generations of CNN hardware are designed in this dissertation. The first two generations are CNN processors based on the conventional Von Neumann architecture, and the third generation CNN hardware is based on in-DRAM processing framework that does not obey Von Neumann architecture. The first generation primitive CNN processor integrates dual-range multiply-accumulate (MAC) blocks by exploiting the statistics of input feature values to reduce energy consumption of MAC operations. Also, tile-based computing method is proposed in the primitive CNN processor. In result, it achieves 1.42TOPS/W energy efficiency in the LeNet-5 CNN model. The second generation advanced CNN processor operates at near-threshold voltage (NTV) to reduce energy consumption furthermore. It also features a newly proposed enhanced output stationary dataflow (EOS) and two-stage big and small on-chip memory architecture, resulting in up to 1.15TOPS/W energy efficiency in the VGG-16 model. Finally, the third generation in-DRAM processing binary CNN hardware processes dominant convolution operations by serially cascading in-DRAM bulk bitwise operations. To this end, we first identify the problem that the bitcount operations with only bulk bitwise AND/OR/NOT incur significant overhead in terms of delay when the size of kernels gets larger. Then, we not only optimize the performance by efficiently allocating inputs and kernels to DRAM banks for both convolutional and fully-connected layers through design space explorations, but also mitigate the overhead of bitcount operations by splitting kernels into multiple parts. Partial sum accumulations and tasks of the other layers such as max-pooling and normalization layers are processed in the peripheral area of DRAM with negligible overheads. In results, our in-DRAM binary CNN processing framework achieves 19x-36x performance and 9x-14x EDP improvements for convolutional layers, and 9x-17x performance and 1.4x-4.5x EDP improvements for fully-connected layers over previous PIM technique in four large-scale CNN models. Also, it shows 3.796TOPS/W energy efficiency in AlexNet CNN model.한국과학기술원 :전기및전자공학부
Alpha particle spectroscopy using FNTD and SIM super-resolution microscopy
No full textStructured illumination microscopy (SIM) for the imaging of alpha particle tracks in fluorescent nuclear track detectors (FNTD) was evaluated and compared to confocal laser scanning microscopy (CLSM). FNTDs were irradiated with an external alpha source and imaged using both methodologies. SIM imaging resulted in improved resolution, without increase in scan time. Alpha particle energy estimation based on the track length, direction and intensity produced results in good agreement with the expected alpha particle energy distribution. A pronounced difference was seen in the spatial scattering of alpha particles in the detectors, where SIM showed an almost 50% reduction compared to CLSM. The improved resolution of SIM allows for more detailed studies of the tracks induced by ionising particles. The combination of SIM and FNTDs for alpha radiation paves the way for affordable and fast alpha spectroscopy and dosimetry. Journal compilatio
A simple disc wind model for broad absorption line quasars
No full textApproximately 20 per cent of quasi-stellar objects (QSOs) exhibit broad, blue-shifted absorption lines in their ultraviolet spectra. Such features provide clear evidence for significant outflows from these systems, most likely in the form of accretion disc winds. These winds may represent the ‘quasar’ mode of feedback that is often invoked in galaxy formation/evolution models, and they are also key to unification scenarios for active galactic nuclei (AGN) and QSOs. To test these ideas, we construct a simple benchmark model of an equatorial, biconical accretion disc wind in a QSO and use a Monte Carlo ionization/radiative transfer code to calculate the ultraviolet spectra as a function of viewing angle. We find that for plausible outflow parameters, sightlines looking directly into the wind cone do produce broad, blue-shifted absorption features in the transitions typically seen in broad absorption line (BAL) QSOs. However, our benchmark model is intrinsically X-ray weak in order to prevent overionization of the outflow, and the wind does not yet produce collisionally excited line emission at the level observed in non-BAL QSOs. As a first step towards addressing these shortcomings, we discuss the sensitivity of our results to changes in the assumed X-ray luminosity and mass-loss rate, Ṁwind. In the context of our adopted geometry, Ṁwind ∼ Ṁacc is required in order to produce significant BAL features. The kinetic luminosity and momentum carried by such outflows would be sufficient to provide significant feedback
Connecticut State Innovation Model (SIM); Proposed framework--revised 4/30/19
No full text1 online resource (32 pages) : color illustrationsFinal version; "This report was prepared by Health Management Associates (HMA), a leading independent national research and consulting firm"--Page 3; "The project described was supported by Funding Opportunity Number CMS-1G1CMS331630-02-00 from the U.S. Department of Health & Human Services, Centers for Medicare & Medicaid Services."; "Approved June 2019."; Includes bibliographical reference
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