210 research outputs found
Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition
Good old on-line back-propagation for plain multi-layer perceptrons yields a
very low 0.35% error rate on the famous MNIST handwritten digits benchmark. All
we need to achieve this best result so far are many hidden layers, many neurons
per layer, numerous deformed training images, and graphics cards to greatly
speed up learning.Comment: 14 pages, 2 figures, 4 listing
Hardware Accelerator Design for Machine Learning
Machine learning is widely used in many modern artificial intelligence applications. Various hardware platforms are implemented to support such applications. Among them, graphics processing unit (GPU) is the most widely used one due to its fast computation speed and compatibility with various algorithms. Field programmable gate arrays (FPGA) show better energy efficiency compared with GPU when computing machine learning algorithm at the cost of low speed. Finally, various application specific integrated circuit (ASIC) architecture is proposed to achieve the best energy efficiency at the cost of less reconfigurability which makes it suitable for special kinds of machine learning algorithms such as a deep convolutional neural network. Finally, analog computing shows a promising methodology to compute large-sized machine learning algorithm due to its low design cost and fast computing speed; however, due to the requirement of the analog-to-digital converter (ADC) in the analog computing, this kind of technique is only applicable to low computation resolution, making it unsuitable for most artificial intelligence (AI) applications
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
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