arXiv (All)
open access
Parametrized Exact Hardware-Software Partitioning
When optimizing computing architecture for specific computationally intensive tasks, such as evaluating or training a neural network, it is important to identify what computational subtasks will offer the greatest decrease in cost (e.g., wall clock time or energy usage). This problem is known as Har
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
- CPUBone: Efficient Vision Backbone Design for Devices with Low Parallelization Capabilities#971779
- Higher-level synthesis methodology for heterogeneous software and hardware architectures#1312755
- Microbenchmark-Driven Analytical Performance Modeling Across Modern GPU Architectures#168296
- PotentialEP: Backprop-Free Training of CIFAR-Scale Generative Models via Equilibrium Propagation#1325305
- AI as a Compiler: Compiling Triton kernels without the Triton compiler#1132001
Record · ID 1218437
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