SegFold achieves 1.95× geometric-mean speedup over prior SpGEMM accelerators via fine-grained dynamic scheduling and remapping in its Segment dataflow.
Invited: Leveraging machine learning for quantum compilation optimization
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UNVERDICTED 3representative citing papers
TWLA is a PTQ method using E2M-ATQ, KOTMS, and ILA-AMP to enable W1.58A4 quantization for LLMs with maintained accuracy.
PennyLang dataset of 3,347 PennyLane samples boosts LLM code generation success via RAG from 8.7% to 41.7% for Qwen 7B and 78.8% to 84.8% for LLaMa 4.
citing papers explorer
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SegFold: Accelerating Sparse GEMM with a Fine-Grained Dynamic Dataflow
SegFold achieves 1.95× geometric-mean speedup over prior SpGEMM accelerators via fine-grained dynamic scheduling and remapping in its Segment dataflow.
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TWLA: Achieving Ternary Weights and Low-Bit Activations for LLMs via Post-Training Quantization
TWLA is a PTQ method using E2M-ATQ, KOTMS, and ILA-AMP to enable W1.58A4 quantization for LLMs with maintained accuracy.
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A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG
PennyLang dataset of 3,347 PennyLane samples boosts LLM code generation success via RAG from 8.7% to 41.7% for Qwen 7B and 78.8% to 84.8% for LLaMa 4.