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Rachmad Vidya Wicaksana Putra

New York University (NYU) Abu Dhabi

43 papers hereh-index 19972 citations78 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author30
  • middle author11

Across the 41 of 43 papers where every author was matched, so the position is known.

fields
  • cs.NE24
  • cs.AR8
  • cs.LG3
  • cs.RO3
  • cs.DC2
  • cs.CR1
affiliations
  • New York University (NYU) Abu Dhabi
HomepageORCID 0000-0001-8597-4530
same name
  • Rachmad Vidya Wicaksana Putra — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182026
most citedFSpiNN: An Optimization Framework for Memory- and Energy-Efficient Spiking Neural Networks

73 citations · 495 across the 39 of their papers we have counts for

collaborators
Showing 2026 · cs.ARShow all

3 papers · 2 filters

cs.AR2026

MorphAtt: A Neuromorphic Accelerator for Efficient Multi-Head Attention Processing in Spiking Vision Transformers

Rachmad Vidya Wicaksana Putra, Amirhesam Jafari Rad, Muhammad Shafique

Spiking Vision Transformers (SViTs) are developed as an energy-efficient alternative to conventional ViTs for computer vision tasks at the edge. However, huge parameter counts and…

cs.AR2026

MDTransformer: A Hardware-Software Co-Design of Mode-Division Photonic Transformer Accelerator with Inverse-Designed Coherent Crossbar

Solomon Micheal Serunjogi, Rachmad Vidya Wicaksana Putra, Ayat Taha +2

Recently, photonic transformer accelerators (PTAs) have successfully achieved significant speedup and energy efficiency improvements over electronic accelerators for expediting Tra…

cs.AR2026

DxPTA: An Architecture Design Space Exploration with Optical Dataflow-guided Strategy for HW/SW Co-Design of Photonic Transformer Accelerators

Rachmad Vidya Wicaksana Putra, Solomon Micheal Serunjogi, Mahmoud Rasras +1

Transformer-based networks have emerged as prominent AI models with state-of-the-art performance, which potentially pave the way toward artificial general intelligence (AGI). Howev…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.