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Abstract: Compute-in-memory (CIM) is a promising approach for realizing energy-efficient deep neural network (DNN) accelerators. Previous CIM works focusing on uniform quantization (UQ) demonstrated a ...
Abstract: In recent years, deep neural networks (DNNs) have experienced rapid development. These DNNs demonstrate significant variations in architecture and scale, creating a substantial demand for ...
This important study presents a meta-analysis confirming a statistically significant association between slow oscillation-spindle coupling and memory formation, although the reported effects are ...
For MultiTableQA, we release a comprehensive benchmark via Hugging Face, constructed from five widely used table QA datasets: TATQA, TabFact, SQA, WTQ, and HybridQA. MultiTableQA extends the ...
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