TFApprox: Towards a Fast Emulation of DNN Approximate Hardware Accelerators on GPU

by   Filip Vaverka, et al.

Energy efficiency of hardware accelerators of deep neural networks (DNN) can be improved by introducing approximate arithmetic circuits. In order to quantify the error introduced by using these circuits and avoid the expensive hardware prototyping, a software emulator of the DNN accelerator is usually executed on CPU or GPU. However, this emulation is typically two or three orders of magnitude slower than a software DNN implementation running on CPU or GPU and operating with standard floating point arithmetic instructions and common DNN libraries. The reason is that there is no hardware support for approximate arithmetic operations on common CPUs and GPUs and these operations have to be expensively emulated. In order to address this issue, we propose an efficient emulation method for approximate circuits utilized in a given DNN accelerator which is emulated on GPU. All relevant approximate circuits are implemented as look-up tables and accessed through a texture memory mechanism of CUDA capable GPUs. We exploit the fact that the texture memory is optimized for irregular read-only access and in some GPU architectures is even implemented as a dedicated cache. This technique allowed us to reduce the inference time of the emulated DNN accelerator approximately 200 times with respect to an optimized CPU version on complex DNNs such as ResNet. The proposed approach extends the TensorFlow library and is available online at


Using Libraries of Approximate Circuits in Design of Hardware Accelerators of Deep Neural Networks

Approximate circuits have been developed to provide good tradeoffs betwe...

ApproxTrain: Fast Simulation of Approximate Multipliers for DNN Training and Inference

Edge training of Deep Neural Networks (DNNs) is a desirable goal for con...

autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components

Approximate computing is an emerging paradigm for developing highly ener...

FATE: Fast and Accurate Timing Error Prediction Framework for Low Power DNN Accelerator Design

Deep neural networks (DNN) are increasingly being accelerated on applica...

Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN

Over the past few years machine learning has seen a renewed explosion of...

DNN Dataflow Choice Is Overrated

Many DNN accelerators have been proposed and built using different micro...

PDFFlow: hardware accelerating parton density access

We present PDFFlow, a new software for fast evaluation of parton distrib...

Please sign up or login with your details

Forgot password? Click here to reset