A Full System Co-Simulation Platform for Evaluating Edge Machine Learning Inference Using Compute-in-Memory

<p>We present a full-system co-simulation platform for evaluating embedded machine learning (ML) inference using compute-in-memory (CIM). CIM architectures aim to reduce data movement overhead by performing matrix operations in memory, but end-to-end benefits depend on system-level integration costs that are difficult to assess with isolated hardware models alone. To address this gap, we develop an integrated RISC-V QEMU-SystemC co-simulation environment that allows standard embedded Linux to interact with a transaction-level CIM accelerator model via memory-mapped I/O, direct memory access (DMA), and interrupts. To evaluate performance, we benchmark an MNIST image inference workload and a synthetic fully connected neural network, comparing CPU-only execution with CIM-offloaded execution. For MNIST, CIM reduces CPU instruction count by 88.6% and estimated total system dynamic energy by 84.0%. Furthermore, stress-testing with the large synthetic topology demonstrates that these efficiency gains scale considerably as the CPU encounters the memory wall. Ultimately, the co-simulation framework provides a practical, full-stack approach for studying CIM integration trade-offs in embedded ML systems and for identifying when CIM-offload becomes beneficial under realistic constraints on software and device interactions.</p>

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical Engineering and Computer Science

Year

2026

Author dc:creator

• Lee, Belsen

Contributors dc:contributor

• Dr. Tom Springer
• Dr. Peiyi Zhao
• Dr. Mark Harrison

• Compute-in-Memory
• Co-Simulation
• QEMU
• SystemC
• RISC-V
• Edge ML
• Computer and Systems Architecture
• Hardware Systems
• VLSI and Circuits, Embedded and Hardware Systems

Repository record dc:identifier

https://digitalcommons.chapman.edu/eecs_theses/10

OAI identifier oai:identifier

oai:digitalcommons.chapman.edu:eecs_theses-1010