# CLSA-CIM: A Cross-Layer Scheduling Approach for Computing-in-Memory Architectures

arXiv: https://arxiv.org/abs/2401.07671 · HTML: https://mlsanity.com/paper/2401.07671/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 15 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [PUMA: A Programmable Ultra-efficient Memristor-based Accelerator for Machine Learning Inference](https://mlsanity.com/paper/1901.10351/) (2019)
- [Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals](https://mlsanity.com/paper/2201.12861/) (2022)
- [Stream: Design Space Exploration of Layer-Fused DNNs on Heterogeneous Dataflow Accelerators](https://mlsanity.com/paper/2212.10612/) (2022)
- [Mapping of CNNs on multi-core RRAM-based CIM architectures](https://mlsanity.com/paper/2309.03805/) (2023)

## Built on by (most cited first, 1 of 1)

- [Mixed-Precision Training and Compilation for RRAM-based Computing-in-Memory Accelerators](https://mlsanity.com/paper/2601.21737/) (2026)

## Abstract (arXiv)

The demand for efficient machine learning (ML) accelerators is growing rapidly, driving the development of novel computing concepts such as resistive random access memory (RRAM)-based tiled computing-in-memory (CIM) architectures. CIM allows to compute within the memory unit, resulting in faster data processing and reduced power consumption. Efficient compiler algorithms are essential to exploit the potential of tiled CIM architectures. While conventional ML compilers focus on code generation for CPUs, GPUs, and other von Neumann architectures, adaptations are needed to cover CIM architectures. Cross-layer scheduling is a promising approach, as it enhances the utilization of CIM cores, thereby accelerating computations. Although similar concepts are implicitly used in previous work, there is a lack of clear and quantifiable algorithmic definitions for cross-layer scheduling for tiled CIM architectures. To close this gap, we present CLSA-CIM, a cross-layer scheduling algorithm for tiled CIM architectures. We integrate CLSA-CIM with existing weight-mapping strategies and compare performance against state-of-the-art (SOTA) scheduling algorithms. CLSA-CIM improves the utilization by up to 17.9 x , resulting in an overall speedup increase of up to 29.2 x compared to SOTA.