17 citations · 25 across the 24 of their papers we have counts for
10 papers · 1 filter
WARD: Runtime Workload-Adaptive Vision TRansformer Framework for Dependable Edge AI
Mahdi Taheri, Pramit Kumar Bhaduri, Mohammad Masoumi +1
Edge-deployed AI operate under dynamically changing power budgets, reliability requirements, and input distributions, requiring continuous adaptation. Such conditions arise in long…
REQAP: Resilient Weight Packing and Quantization for Edge DNN Acceleration
Mahdi Taheri, Samira Nazari, Mubassher Ansari +4
Efficient deployment of Deep Neural Networks (DNNs) on edge accelerators requires aggressive model compression while maintaining reliability in fault-prone hardware environments. T…
An FPGA-Based SoC Architecture with a RISC-V Controller for Energy-Efficient Temporal-Coding Spiking Neural Networks
Mohammad Javad Sekonji, Ali Mahani, Maryam Mirsadeghi +1
Spiking Neural Networks (SNNs) offer high energy efficiency and event-driven computation, ideal for low-power edge AI. Their hardware implementation on FPGAs, however, faces challe…
Sensitivity-Guided Framework for Pruned and Quantized Reservoir Computing Accelerators
Atousa Jafari, Mahdi Taheri, Hassan Ghasemzadeh Mohammadi +2
This paper presents a compression framework for Reservoir Computing that enables systematic design-space exploration of trade-offs among quantization levels, pruning rates, model a…
DART: Input-Difficulty-AwaRe Adaptive Threshold for Early-Exit DNNs
Parth Patne, Mahdi Taheri, Christian Herglotz +3
Early-exit deep neural networks enable adaptive inference by terminating computation when sufficient confidence is achieved, reducing cost for edge AI accelerators in resource-cons…
PhD Thesis Summary: Methods for Reliability Assessment and Enhancement of Deep Neural Network Hardware Accelerators
Mahdi Taheri
This manuscript summarizes the work and showcases the impact of the doctoral thesis by introducing novel, cost-efficient methods for assessing and enhancing the reliability of DNN…