Zusammenfassung
Deep neural networks (DNNs) are used by different applications that are
executed on a range of computer architectures, from IoT devices to
supercomputers. The footprint of these networks is huge as well as their
computational and communication needs. In order to ease the pressure on
resources, research indicates that in many cases a low precision representation
(1-2 bit per parameter) of weights and other parameters can achieve similar
accuracy while requiring less resources. Using quantized values enables the use
of FPGAs to run NNs, since FPGAs are well fitted to these primitives; e.g.,
FPGAs provide efficient support for bitwise operations and can work with
arbitrary-precision representation of numbers.
This paper presents a new streaming architecture for running QNNs on FPGAs.
The proposed architecture scales out better than alternatives, allowing us to
take advantage of systems with multiple FPGAs. We also included support for
skip connections, that are used in state-of-the art NNs, and shown that our
architecture allows to add those connections almost for free. All this allowed
us to implement an 18-layer ResNet for 224x224 images classification, achieving
57.5% top-1 accuracy.
In addition, we implemented a full-sized quantized AlexNet. In contrast to
previous works, we use 2-bit activations instead of 1-bit ones, which improves
AlexNet's top-1 accuracy from 41.8% to 51.03% for the ImageNet classification.
Both AlexNet and ResNet can handle 1000-class real-time classification on an
FPGA.
Our implementation of ResNet-18 consumes 5x less power and is 4x slower for
ImageNet, when compared to the same NN on the latest Nvidia GPUs. Smaller NNs,
that fit a single FPGA, are running faster then on GPUs on small (32x32)
inputs, while consuming up to 20x less energy and power.
Nutzer