RapidChiplet: A Toolchain for Rapid Design Space Exploration of Chiplet Architectures
By Patrick Iff, Benigna Bruggmann, Maciej Besta, Luca Benini, Torsten Hoefler (ETH Zurich)
Chiplet architectures are a promising paradigm to overcome the scaling challenges of monolithic chips. Chiplets offer heterogeneity, modularity, and cost-effectiveness. The design space of chiplet architectures is huge as there are many degrees of freedom such as the number, size and placement of chiplets, the topology of the inter-chiplet interconnect and many more. Existing tools for cost and performance prediction are often too slow to explore this design space. We present RapidChiplet, a fast, open-source toolchain to predict latency and throughput of the inter-chiplet interconnect, as well as a chip's manufacturing cost and thermal stability.
To read the full article, click here
Related Chiplet
- Integrated voltage regulator (IVR) chiplet
- High-performance connectivity chiplets
- eFPGA Chiplet
- DPIQ Tx PICs
- IMDD Tx PICs
Related Technical Papers
- ThermoDSE: A Thermal-Aware and Comprehensive Design Space Exploration for Chiplet-Based DNN Accelerators
- DeepStack: Scalable and Accurate Design Space Exploration for Distributed 3D-Stacked AI Accelerators
- Beacon: LLM Multi-Agent Driven Hardware Design Space Exploration for Heterogeneous Multi-Chiplet Deep Learning Accelerators
- Muchisim: A Simulation Framework for Design Exploration of Multi-Chip Manycore Systems
Latest Technical Papers
- Chiplet-Based Techniques for Scalable and Memory-Aware Multiscalar Multiplication on Hardware Platforms
- A Time-Encoded Analog Photonic Interposer for Energy-Efficient Integration of Analog Vision Sensors and Analog Accelerators
- Self-Activated Direct Bonding with a Highly Polar Atomic-Layer-Deposited Film for 3D Integration
- FLINT: Efficiently Leveraging High Bandwidth Flash for Capacity-Scalable LLM Inference Acceleration
- Beacon: LLM Multi-Agent Driven Hardware Design Space Exploration for Heterogeneous Multi-Chiplet Deep Learning Accelerators