Vision Transformers (ViTs) demonstrate exceptional performance in computer vision but suffer from large parameter counts and quadratic computational complexity, severely limiting their deployment on resource-constrained edge hardware. While recursive weight-sharing reduces parameter counts and token merging mitigates c...
Junseong Kim, Uraz Odyurt, A. Yousefzadeh· 0 citations
This survey reviews 71 studies published between 2015 and 2025 and organizes them into a taxonomy that classifies GPU works into simulation frameworks, training-acceleration techniques, large-scale and multi-GPU simulation, and application deployments, and RISC-V works into instruction-set extensions and tightly-couple...
Edris Zaman Farsa, Amirhossein Ilkhani, Marc Reichenbach et al.· Neuromorphic Computing and E...· 0 citations
This study investigates the most recent advances, trends, and design choices for Transformer inference on FPGA platforms, and performs a systematic literature review, extracting and delving into preferred techniques for implementation and optimisation.
Arjan Blankestijn, Uraz Odyurt, A. Yousefzadeh· Journal of systems architect...· 0 citations
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