96,312 research outputs found
An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration
We empirically evaluate an undervolting technique, i.e., underscaling the
circuit supply voltage below the nominal level, to improve the power-efficiency
of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable
Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing
faults due to excessive circuit latency increase. We evaluate the
reliability-power trade-off for such accelerators. Specifically, we
experimentally study the reduced-voltage operation of multiple components of
real FPGAs, characterize the corresponding reliability behavior of CNN
accelerators, propose techniques to minimize the drawbacks of reduced-voltage
operation, and combine undervolting with architectural CNN optimization
techniques, i.e., quantization and pruning. We investigate the effect of
environmental temperature on the reliability-power trade-off of such
accelerators. We perform experiments on three identical samples of modern
Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification
CNN benchmarks. This approach allows us to study the effects of our
undervolting technique for both software and hardware variability. We achieve
more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain
is the result of eliminating the voltage guardband region, i.e., the safe
voltage region below the nominal level that is set by FPGA vendor to ensure
correct functionality in worst-case environmental and circuit conditions. 43%
of the power-efficiency gain is due to further undervolting below the
guardband, which comes at the cost of accuracy loss in the CNN accelerator. We
evaluate an effective frequency underscaling technique that prevents this
accuracy loss, and find that it reduces the power-efficiency gain from 43% to
25%.Comment: To appear at the DSN 2020 conferenc
FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture
Neural Network (NN) accelerators with emerging ReRAM (resistive random access
memory) technologies have been investigated as one of the promising solutions
to address the \textit{memory wall} challenge, due to the unique capability of
\textit{processing-in-memory} within ReRAM-crossbar-based processing elements
(PEs). However, the high efficiency and high density advantages of ReRAM have
not been fully utilized due to the huge communication demands among PEs and the
overhead of peripheral circuits.
In this paper, we propose a full system stack solution, composed of a
reconfigurable architecture design, Field Programmable Synapse Array (FPSA) and
its software system including neural synthesizer, temporal-to-spatial mapper,
and placement & routing. We highly leverage the software system to make the
hardware design compact and efficient. To satisfy the high-performance
communication demand, we optimize it with a reconfigurable routing architecture
and the placement & routing tool. To improve the computational density, we
greatly simplify the PE circuit with the spiking schema and then adopt neural
synthesizer to enable the high density computation-resources to support
different kinds of NN operations. In addition, we provide spiking memory blocks
(SMBs) and configurable logic blocks (CLBs) in hardware and leverage the
temporal-to-spatial mapper to utilize them to balance the storage and
computation requirements of NN. Owing to the end-to-end software system, we can
efficiently deploy existing deep neural networks to FPSA. Evaluations show
that, compared to one of state-of-the-art ReRAM-based NN accelerators, PRIME,
the computational density of FPSA improves by 31x; for representative NNs, its
inference performance can achieve up to 1000x speedup.Comment: Accepted by ASPLOS 201
Ring oscillator clocks and margins
How much margin do we have to add to the delay lines of a bundled-data circuit? This paper is an attempt to give a methodical answer to this question, taking into account all sources of variability and the existing EDA machinery for timing analysis and sign-off. The paper is based on the study of the margins of a ring oscillator that substitutes a PLL as clock generator. A timing model is proposed that shows that a 12% margin for delay lines can be sufficient to cover variability in a 65nm technology. In a typical scenario, performance and energy improvements between 15% and 35% can be obtained by using a ring oscillator instead of a PLL. The paper concludes that a synchronous circuit with a ring oscillator clock shows similar benefits in performance and energy as those of bundled-data asynchronous circuits.Peer ReviewedPostprint (author's final draft
The PreAmplifier ShAper for the ALICE TPC-Detector
In this paper the PreAmplifier ShAper (PASA) for the Time Projection Chamber
(TPC) of the ALICE experiment at LHC is presented. The ALICE TPC PASA is an
ASIC that integrates 16 identical channels, each consisting of Charge Sensitive
Amplifiers (CSA) followed by a Pole-Zero network, self-adaptive bias network,
two second-order bridged-T filters, two non-inverting level shifters and a
start-up circuit. The circuit is optimized for a detector capacitance of 18-25
pF. For an input capacitance of 25 pF, the PASA features a conversion gain of
12.74 mV/fC, a peaking time of 160 ns, a FWHM of 190 ns, a power consumption of
11.65 mW/ch and an equivalent noise charge of 244e + 17e/pF. The circuit
recovers smoothly to the baseline in about 600 ns. An integral non-linearity of
0.19% with an output swing of about 2.1 V is also achieved. The total area of
the chip is 18 mm and is implemented in AMS's C35B3C1 0.35 micron CMOS
technology. Detailed characterization test were performed on about 48000 PASA
circuits before mounting them on the ALICE TPC front-end cards. After more than
two years of operation of the ALICE TPC with p-p and Pb-Pb collisions, the PASA
has demonstrated to fulfill all requirements
An Energy and Performance Exploration of Network-on-Chip Architectures
In this paper, we explore the designs of a circuit-switched router, a wormhole router, a quality-of-service (QoS) supporting virtual channel router and a speculative virtual channel router and accurately evaluate the energy-performance tradeoffs they offer. Power results from the designs placed and routed in a 90-nm CMOS process show that all the architectures dissipate significant idle state power. The additional energy required to route a packet through the router is then shown to be dominated by the data path. This leads to the key result that, if this trend continues, the use of more elaborate control can be justified and will not be immediately limited by the energy budget. A performance analysis also shows that dynamic resource allocation leads to the lowest network latencies, while static allocation may be used to meet QoS goals. Combining the power and performance figures then allows an energy-latency product to be calculated to judge the efficiency of each of the networks. The speculative virtual channel router was shown to have a very similar efficiency to the wormhole router, while providing a better performance, supporting its use for general purpose designs. Finally, area metrics are also presented to allow a comparison of implementation costs
Statistical Power Supply Dynamic Noise Prediction in Hierarchical Power Grid and Package Networks
One of the most crucial high performance systems-on-chip design challenge is to front their power supply noise sufferance due to high frequencies, huge number of functional blocks and technology scaling down. Marking a difference from traditional post physical-design static voltage drop analysis, /a priori dynamic voltage drop/evaluation is the focus of this work. It takes into account transient currents and on-chip and package /RLC/ parasitics while exploring the power grid design solution space: Design countermeasures can be thus early defined and long post physical-design verification cycles can be shortened. As shown by an extensive set of results, a carefully extracted and modular grid library assures realistic evaluation of parasitics impact on noise and facilitates the power network construction; furthermore statistical analysis guarantees a correct current envelope evaluation and Spice simulations endorse reliable result
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