This paper presents FORTUNE, a hardware-agnostic fault tolerance technique for DNNs that leverages quantization to enhance reliability without significant performance overhead. Unlike conventional methods like Triple Modular Redundancy (TMR), which are computationally expensive, the proposed approach uses memory savings from quantization to protect the critical Most Significant Bit, improving fault tolerance in Deep Neural Networks (DNNs). Memory utilization has been reduced by 37.5% across all networks, with vulnerability in AlexNet reduced by 56% compared to the 8-bit version and 84% compared to the unprotected 3-bit version. These improvements come with only a minor increase in execution time of less than 3%. Using AlexNet as an example demonstrates how our approach effectively enhances memory utilization and resilience while causing only a minimal increase in execution time.

Multiplication is the most resource-hungry operation in the neural network’s processing elements. In this paper, we propose an architecture of a novel adaptive fault-tolerant approximate multiplier tailored for ASIC-based DNN accelerators. AdAM employs an adaptive adder relying on an unconventional use of the leading one position value of the inputs for fault detection through the optimization of unutilized adder resources. The proposed architecture uses a lightweight fault mitigation technique that sets the detected faulty bits to zero. The hardware resource utilization and the DNN accelerator’s reliability metrics are used to compare the proposed solution against the triple modular redundancy (TMR) in multiplication, unprotected exact multiplication, and unprotected approximate multiplication. It is demonstrated that the proposed architecture enables a multiplication with a reliability level close to the multipliers protected by TMR utilizing 63.54% less area and having 39.06% lower power-delay product compared to the exact multiplier.