  The stringent requirements for the Deep Neural Networks (DNNs) accelerator's
reliability stand along with the need for reducing the computational burden on
the hardware platforms, i.e. reducing the energy consumption and execution time
as well as increasing the efficiency of DNN accelerators. Moreover, the growing
demand for specialized DNN accelerators with tailored requirements,
particularly for safety-critical applications, necessitates a comprehensive
design space exploration to enable the development of efficient and robust
accelerators that meet those requirements. Therefore, the trade-off between
hardware performance, i.e. area and delay, and the reliability of the DNN
accelerator implementation becomes critical and requires tools for analysis.
This paper presents a comprehensive methodology for exploring and enabling a
holistic assessment of the trilateral impact of quantization on model accuracy,
activation fault reliability, and hardware efficiency. A fully automated
framework is introduced that is capable of applying various quantization-aware
techniques, fault injection, and hardware implementation, thus enabling the
measurement of hardware parameters. Moreover, this paper proposes a novel
lightweight protection technique integrated within the framework to ensure the
dependable deployment of the final systolic-array-based FPGA implementation.
The experiments on established benchmarks demonstrate the analysis flow and the
profound implications of quantization on reliability, hardware performance, and
network accuracy, particularly concerning the transient faults in the network's
activations.
