Gemini: Mapping and Architecture Co-exploration for Large-scale DNN Chiplet Accelerators
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Abstract (arXiv)
Chiplet technology enables the integration of an increasing number of transistors on a single accelerator with higher yield in the post-Moore era, addressing the immense computational demands arising from rapid AI advancements. However, it also introduces more expensive packaging costs and costly Die-to-Die (D2D) interfaces, which require more area, consume higher power, and offer lower bandwidth than on-chip interconnects. Maximizing the benefits and minimizing the drawbacks of chiplet technology is crucial for developing large-scale DNN chiplet accelerators, which poses challenges to both architecture and mapping. Despite its importance in the post-Moore era, methods to address these challenges remain scarce.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Dec 2023.
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Citing papers per year: 2024: 2; 2025: 4; 2026: 1.
7 papers.
- 2025Embodied AI: From LLMs to World Modelscited by 24
- 2024SCAR: Scheduling Multi-Model AI Workloads on Heterogeneous Multi-Chiplet Module Acceleratorscited by 6
- 2025ADOR: A Design Exploration Framework for LLM Serving with Enhanced Latency and Throughputcited by 4
- 2024Performance Implications of Multi-Chiplet Neural Processing Units on Autonomous Driving Perceptioncited by 1
- 2025Exploring the Potential of Wireless-enabled Multi-Chip AI Acceleratorscited by 0
- 2025Mozart: A Chiplet Ecosystem-Accelerator Codesign Framework for Composable Bespoke Application Specific Integrated Circuitscited by 0
- 2026HYDRA: A Heterogeneous Chiplet DSE Framework for Serving Dynamic Hybrid LLM Workloadscited by 0
- 2026HYDRA: A Heterogeneous Chiplet DSE Framework for Serving Dynamic Hybrid LLM Workloads19 Aug 2026
- 2025Mozart: A Chiplet Ecosystem-Accelerator Codesign Framework for Composable Bespoke Application Specific Integrated Circuits10 Oct 2025
- 2025Embodied AI: From LLMs to World Models24 Sep 2025
- 2025ADOR: A Design Exploration Framework for LLM Serving with Enhanced Latency and Throughput6 Mar 2025
- 2025Exploring the Potential of Wireless-enabled Multi-Chip AI Accelerators29 Jan 2025
- 2024Performance Implications of Multi-Chiplet Neural Processing Units on Autonomous Driving Perception24 Nov 2024
- 2024SCAR: Scheduling Multi-Model AI Workloads on Heterogeneous Multi-Chiplet Module Accelerators1 May 2024