4,752 research outputs found
Diversification Across Mining Pools: Optimal Mining Strategies under PoW
Mining is a central operation of all proof-of-work (PoW) based
cryptocurrencies. The vast majority of miners today participate in "mining
pools" instead of "solo mining" in order to lower risk and achieve a more
steady income. However, this rise of participation in mining pools negatively
affects the decentralization levels of most cryptocurrencies. In this work, we
look into mining pools from the point of view of a miner: We present an
analytical model and implement a computational tool that allows miners to
optimally distribute their computational power over multiple pools and PoW
cryptocurrencies (i.e. build a mining portfolio), taking into account their
risk aversion levels. Our tool allows miners to maximize their risk-adjusted
earnings by diversifying across multiple mining pools which enhances PoW
decentralization. Finally, we run an experiment in Bitcoin historical data and
demonstrate that a miner diversifying over multiple pools, as instructed by our
model/tool, receives a higher overall Sharpe ratio (i.e. average excess reward
over its standard deviation/volatility).Comment: 13 pages, 16 figures. Presented at WEIS 201
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
Infrastructure for Detector Research and Development towards the International Linear Collider
The EUDET-project was launched to create an infrastructure for developing and
testing new and advanced detector technologies to be used at a future linear
collider. The aim was to make possible experimentation and analysis of data for
institutes, which otherwise could not be realized due to lack of resources. The
infrastructure comprised an analysis and software network, and instrumentation
infrastructures for tracking detectors as well as for calorimetry.Comment: 54 pages, 48 picture
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