# TDO-CIM: Transparent Detection and Offloading for Computation In-memory

arXiv: https://arxiv.org/abs/2007.00060 · HTML: https://mlsanity.com/paper/2007.00060/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [A Reinforcement Learning Environment for Polyhedral Optimizations](https://mlsanity.com/paper/2104.13732/) (2021)
- [Optimizing Binary and Ternary Neural Network Inference on RRAM Crossbars using CIM-Explorer](https://mlsanity.com/paper/2505.14303/) (2025)
- [Mixed-Precision Training and Compilation for RRAM-based Computing-in-Memory Accelerators](https://mlsanity.com/paper/2601.21737/) (2026)

## Abstract (arXiv)

Computation in-memory is a promising non-von Neumann approach aiming at completely diminishing the data transfer to and from the memory subsystem. Although a lot of architectures have been proposed, compiler support for such architectures is still lagging behind. In this paper, we close this gap by proposing an end-to-end compilation flow for in-memory computing based on the LLVM compiler infrastructure. Starting from sequential code, our approach automatically detects, optimizes, and offloads kernels suitable for in-memory acceleration. We demonstrate our compiler tool-flow on the PolyBench/C benchmark suite and evaluate the benefits of our proposed in-memory architecture simulated in Gem5 by comparing it with a state-of-the-art von Neumann architecture.