Date of Award
Spring 5-2026
Document Type
Thesis
Department
Electrical Engineering and Computer Science
First Advisor
Dr. Tom Springer
Second Advisor
Dr. Peiyi Zhao
Third Advisor
Dr. Mark Harrison
Abstract
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.
DOI
10.36837/chapman.000735
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.
Recommended Citation
B. Lee, "A full system co-simulation platform for evaluating edge machine learning inference using compute-in-memory," M. S. thesis, Chapman University, Orange, CA, 2026. https://doi.org/10.36837/chapman.000735
Included in
Computer and Systems Architecture Commons, Hardware Systems Commons, VLSI and Circuits, Embedded and Hardware Systems Commons