124 research outputs found
EIE: Efficient Inference Engine on Compressed Deep Neural Network
State-of-the-art deep neural networks (DNNs) have hundreds of millions of
connections and are both computationally and memory intensive, making them
difficult to deploy on embedded systems with limited hardware resources and
power budgets. While custom hardware helps the computation, fetching weights
from DRAM is two orders of magnitude more expensive than ALU operations, and
dominates the required power.
Previously proposed 'Deep Compression' makes it possible to fit large DNNs
(AlexNet and VGGNet) fully in on-chip SRAM. This compression is achieved by
pruning the redundant connections and having multiple connections share the
same weight. We propose an energy efficient inference engine (EIE) that
performs inference on this compressed network model and accelerates the
resulting sparse matrix-vector multiplication with weight sharing. Going from
DRAM to SRAM gives EIE 120x energy saving; Exploiting sparsity saves 10x;
Weight sharing gives 8x; Skipping zero activations from ReLU saves another 3x.
Evaluated on nine DNN benchmarks, EIE is 189x and 13x faster when compared to
CPU and GPU implementations of the same DNN without compression. EIE has a
processing power of 102GOPS/s working directly on a compressed network,
corresponding to 3TOPS/s on an uncompressed network, and processes FC layers of
AlexNet at 1.88x10^4 frames/sec with a power dissipation of only 600mW. It is
24,000x and 3,400x more energy efficient than a CPU and GPU respectively.
Compared with DaDianNao, EIE has 2.9x, 19x and 3x better throughput, energy
efficiency and area efficiency.Comment: External Links: TheNextPlatform: http://goo.gl/f7qX0L ; O'Reilly:
https://goo.gl/Id1HNT ; Hacker News: https://goo.gl/KM72SV ; Embedded-vision:
http://goo.gl/joQNg8 ; Talk at NVIDIA GTC'16: http://goo.gl/6wJYvn ; Talk at
Embedded Vision Summit: https://goo.gl/7abFNe ; Talk at Stanford University:
https://goo.gl/6lwuer. Published as a conference paper in ISCA 201
Self-sustaining Ultra-wideband Positioning System for Event-driven Indoor Localization
Smart and unobtrusive mobile sensor nodes that accurately track their own
position have the potential to augment data collection with location-based
functions. To attain this vision of unobtrusiveness, the sensor nodes must have
a compact form factor and operate over long periods without battery recharging
or replacement. This paper presents a self-sustaining and accurate
ultra-wideband-based indoor location system with conservative infrastructure
overhead. An event-driven sensing approach allows for balancing the limited
energy harvested in indoor conditions with the power consumption of
ultra-wideband transceivers. The presented tag-centralized concept, which
combines heterogeneous system design with embedded processing, minimizes idle
consumption without sacrificing functionality. Despite modest infrastructure
requirements, high localization accuracy is achieved with error-correcting
double-sided two-way ranging and embedded optimal multilateration. Experimental
results demonstrate the benefits of the proposed system: the node achieves a
quiescent current of and operates at while performing
energy harvesting and motion detection. The energy consumption for position
updates, with an accuracy of (2D) in realistic non-line-of-sight
conditions, is . In an asset tracking case study within a
multi-room office space, the achieved accuracy level allows for identifying 36
different desk and storage locations with an accuracy of over . The
system`s long-time self-sustainability has been analyzed over in
multiple indoor lighting situations
Implementing an Integrated Signaling and Power Distribution Control System for Remotely Located Devices
A system was designed and implemented that combined the distribution of high-current power with a digital control signal over a common conductor. Two different versions of this system were implemented. Initially, a design based on of commercially available parts was created and tested to prove that the concept of combining communications and power is valid. The resulting design was then miniaturized to show that the system might be combined onto a single integrated circuit. In the miniaturization process, some circuit blocks were redesigned to take advantage of the flexibility provided by ASIC designs. Both the proof of concept and the VLSI implementations were completely designed, implemented, and fully tested. It was shown that the system can be miniaturized. The miniaturization provided the advantages of smaller overall implementation size and higher reliability due to decreased part count. The disadvantage of the miniaturization process was that the design became fixed once it was fabricated in silicon
Cosmic Origins (COR) Program Technology Development 2018
No abstract availabl
The Large Array Survey Telescope -- System Overview and Performances
The Large Array Survey Telescope (LAST) is a wide-field visible-light
telescope array designed to explore the variable and transient sky with a high
cadence. LAST will be composed of 48, 28-cm f/2.2 telescopes (32 already
installed) equipped with full-frame backside-illuminated cooled CMOS detectors.
Each telescope provides a field of view (FoV) of 7.4 deg^2 with 1.25
arcsec/pix, while the system FoV is 355 deg^2 in 2.9 Gpix. The total collecting
area of LAST, with 48 telescopes, is equivalent to a 1.9-m telescope. The
cost-effectiveness of the system (i.e., probed volume of space per unit time
per unit cost) is about an order of magnitude higher than most existing and
under-construction sky surveys. The telescopes are mounted on 12 separate
mounts, each carrying four telescopes. This provides significant flexibility in
operating the system. The first LAST system is under construction in the
Israeli Negev Desert, with 32 telescopes already deployed. We present the
system overview and performances based on the system commissioning data. The Bp
5-sigma limiting magnitude of a single 28-cm telescope is about 19.6 (21.0), in
20 s (20x20 s). Astrometric two-axes precision (rms) at the bright-end is about
60 (30)\,mas in 20\,s (20x20 s), while absolute photometric calibration,
relative to GAIA, provides ~10 millimag accuracy. Relative photometric
precision, in a single 20 s (320 s) image, at the bright-end measured over a
time scale of about 60 min is about 3 (1) millimag. We discuss the system
science goals, data pipelines, and the observatory control system in companion
publications.Comment: Submitted to PASP, 15p
Nano-Watt Modular Integrated Circuits for Wireless Neural Interface.
In this work, a nano-watt modular neural interface circuit is proposed for ECoG neuroprosthetics. The main purposes of this work are threefold: (1) optimizing the power-performance of the neural interface circuits based on ECoG signal characteristics, (2) equipping a stimulation capability, and (3) providing a modular system solution to expand functionality.
To achieve these aims, the proposed system introduces the following contributions/innovations: (1) power-noise optimization based on the ECoG signal driven analysis, (2) extreme low-power analog front-ends, (3) Manchester clock-edge modulation clock data recovery, (4) power-efficient data compression, (5) integrated stimulator with fully programmable waveform, (6) wireless signal transmission through skin, and (7) modular expandable design. Towards these challenges and contributions, three different ECoG neural interface systems, ENI-1, ENI-16, and ENI-32, have been designed, fabricated, and tested.
The first ENI system(ENI-1) is a one-channel analog front-end and fabricated in a 0.25µm CMOS process with chopper stabilized pseudo open-loop preamplifier and area-efficient SAR ADC. The measured channel power, noise and area are 1.68µW at 2.5V power-supply, 1.69µVrms (NEF=2.43), and 0.0694mm^2, respectively. The fabricated IC is packaged with customized miniaturized package. In-vivo human EEG is successfully measured with the fabricated ENI-1-IC.
To demonstrate a system expandability and wireless link, ENI-16 IC is fabricated in 0.25µm CMOS process and has sixteen channels with a push-pull preamplifier, asynchronous SAR ADC, and intra-skin communication(ISCOM) which is a new way of transmitting the signal through skin. The measured channel power, noise and area are 780nW, 4.26µVrms (NEF=5.2), and 2.88mm^2, respectively. With the fabricated ENI-16-IC, in-vivo epidural ECoG from monkey is successfully measured.
As a closed-loop system, ENI-32 focuses on optimizing the power performance based on a bio-signal property and integrating stimulator. ENI-32 is fabricated in 0.18µm CMOS process and has thirty-two recording channels and four stimulation channels with a cyclic preamplifier, data compression, asymmetric wireless transceiver (Tx/Rx). The measured channel power, noise and area are 140nW (680nW including ISCOM), 3.26µVrms (NEF=1.6), and 5.76mm^2, respectively. The ENI-32 achieves an order of magnitude power reduction while maintaining the system performance. The proposed nano-watt ENI-32 can be the first practical wireless closed-loop solution with a practically miniaturized implantable device.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/98064/1/schang_1.pd
Design, construction and operation of the ProtoDUNE-SP Liquid Argon TPC
The ProtoDUNE-SP detector is a single-phase liquid argon time projection
chamber (LArTPC) that was constructed and operated in the CERN North Area at
the end of the H4 beamline. This detector is a prototype for the first far
detector module of the Deep Underground Neutrino Experiment (DUNE), which will
be constructed at the Sandford Underground Research Facility (SURF) in Lead,
South Dakota, USA. The ProtoDUNE-SP detector incorporates full-size components
as designed for DUNE and has an active volume of ~m.
The H4 beam delivers incident particles with well-measured momenta and
high-purity particle identification. ProtoDUNE-SP's successful operation
between 2018 and 2020 demonstrates the effectiveness of the single-phase far
detector design. This paper describes the design, construction, assembly and
operation of the detector components
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