# COSMOS: Coordination of High-Level Synthesis and Memory Optimization for Hardware Accelerators

arXiv: https://arxiv.org/abs/1912.10823 · HTML: https://mlsanity.com/paper/1912.10823/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Revisiting MAE pre-training for 3D medical image segmentation](https://mlsanity.com/paper/2410.23132/) (2024)
- [How Confident are Video Models? Empowering Video Models to Express their Uncertainty](https://mlsanity.com/paper/2510.02571/) (2025)
- [Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance](https://mlsanity.com/paper/2409.06171/) (2024)
- [Following the Whispers of Values: Unraveling Neural Mechanisms Behind Value-Oriented Behaviors in LLMs](https://mlsanity.com/paper/2504.04994/) (2025)

## Abstract (arXiv)

Hardware accelerators are key to the efficiency and performance of system-on-chip (SoC) architectures. With high-level synthesis (HLS), designers can easily obtain several performance-cost trade-off implementations for each component of a complex hardware accelerator. However, navigating this design space in search of the Pareto-optimal implementations at the system level is a hard optimization task. We present COSMOS, an automatic methodology for the design-space exploration (DSE) of complex accelerators, that coordinates both HLS and memory optimization tools in a compositional way. First, thanks to the co-design of datapath and memory, COSMOS produces a large set of Pareto-optimal implementations for each component of the accelerator. Then, COSMOS leverages compositional design techniques to quickly converge to the desired trade-off point between cost and performance at the system level. When applied to the system-level design (SLD) of an accelerator for wide-area motion imagery (WAMI), COSMOS explores the design space as completely as an exhaustive search, but it reduces the number of invocations to the HLS tool by up to 14.6x.