LEAF: LUT-aware Efficient Attention on FPGAs
We introduce Lookup-table (LUT)-aware Efficient Attention on FPGAs (LEAF).
ProofPaper ↗
Key points
- LEAF is the first nanosecond-scale LUT-aware transformer for FPGAs.
- LEAF converts the query, key, and value projections and the final output feed-forward layer into FPGA-native layers with LUT-aware training, creating the first LUT-aware transformer and linformer layers built for ultra-low-latency inference.
- Attention resists LUT-aware training because it multiplies two activations where the method needs a constant weight to tabulate; LEAF tabulates the attention block's constant matrix-vector multiplications and leaves the rest in arithmetic form.
- Our most efficient LEAF-based linformer with 64 input particles reaches 80.4% classification accuracy at 28k LUTs and 35ns.
Sources (1)
- [1]LEAF: LUT-aware Efficient Attention on FPGAsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:00 PM
We introduce Lookup-table (LUT)-aware Efficient Attention on FPGAs (LEAF).
LEAF is the first nanosecond-scale LUT-aware transformer for FPGAs.
Extractive summary: sentences quoted from the sources.
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