arXiv (All)
open access
Compute-in-Memory Attention: A Time-Domain Analog Softmax Circuit with RC-Tunable Temperature
Softmax is a key operation in Transformer attention, but its exponentiation and normalization add significant overhead in compute-in-memory (CIM) accelerators, especially when analog attention scores must first be converted to the digital domain. This work presents a tunable-temperature analog softm
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Accuracy and Resiliency of Analog Compute-in-Memory Inference Engines#619960
- Official Review · ICLR 2025#787122
- MoE-CORE: Coordinated Expert Offloading and Residency for Memory-Constrained MoE Inference#1160970
- MoE-CORE: Coordinated Expert Offloading and Residency for Memory-Constrained MoE Inference#1157221
- MemCalib: Benchmarking and Optimizing Memory Use in LLM Agents#1061792
Record · ID 927564
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive