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\author[1]{Mahdi Taheri}%
\author[1]{Natalia Cherezova}%
\author[2]{Samira Nazari}%
\author[1,2]{Ali Azarpeyvand}%
\author[1]{Tara Ghasempouri}%
\author[1,3]{Masoud Daneshtalab}%
\author[1]{Jaan Raik}%
\author[1]{Maksim Jenihhin}%
\affil[1]{Tallinn University of Technology}%
\affil[2]{University of Zanjan}%
\affil[3]{Mälardalen University}%


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  \date{April 30, 2026}


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\begin{abstract}
Deep Neural Network (DNN) hardware accelerators are essential in a
spectrum of safety-critical edge-AI applications with stringent
reliability, energy efficiency, and latency requirements. Multiplication
is the most resource-hungry operation in the neural network's processing
elements. This paper proposes a scalable adaptive fault-tolerant
approximate multiplier (AdAM) tailored for ASIC-based DNN accelerators
at the algorithm and circuit levels. AdAM employs an adaptive adder that
relies on an unconventional use of input Leading One Detector (LOD)
values for fault detection by optimizing unutilized adder resources. A
gate-level optimized LOD design and a hybrid adder design are also
proposed as a part of the adaptive multiplier to improve the hardware
performance. 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 while
at the same time utilizing 2.74 \selectlanguage{ngerman}× less area and with 39.06\% less
power-delay product compared to the exact multiplier. Moreover, it has
similar area, delay, and power consumption parameters compared to the
state-of-the-art approximate multipliers with similar accuracy while
providing fault detection and mitigation capability.%
\end{abstract}\selectlanguage{ngerman}%



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