11 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.CL2026
FlashAttention-4: Algorithm and Kernel Pipelining Co-Design for Asymmetric Hardware Scaling
Ted Zadouri, Markus Hoehnerbach, Jay Shah +3
Attention, as a core layer of the ubiquitous Transformer architecture, is the bottleneck for large language models and long-context applications. While FlashAttention-3 optimized a…
cs.LG2025
Hardware-Efficient Attention for Fast Decoding
Ted Zadouri, Hubert Strauss, Tri Dao
LLM decoding is bottlenecked for large batches and long contexts by loading the key-value (KV) cache from high-bandwidth memory, which inflates per-token latency, while the sequent…
cs.CL2023★ 11 cited
Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning
Ted Zadouri, Ahmet Üstün, Arash Ahmadian +3
The Mixture of Experts (MoE) is a widely known neural architecture where an ensemble of specialized sub-models optimizes overall performance with a constant computational cost. How…