MARVEL: An End-to-End Framework for Generating Model-Class Aware Custom RISC-V Extensions for Lightweight AI

Deploying deep neural networks (DNNs) on resource-constrained IoT devices remains a challenging problem, often requiring hardware modifications tailored to individual AI models. Existing accelerator-generation tools, such as AMD&#x2019;s FINN, do not adequately address extreme resource limitations faced by IoT endpoints operating in bare-metal environments without an operating system (OS). To overcome these constraints, we propose MARVEL&#x2013;an automated, end-to-end framework that generates custom RISC-V ISA extensions tailored to specific DNN model classes, with a primary focus on convolutional neural networks (CNNs). The proposed method profiles high-level DNN representations in Python and generates an ISA-extended RISC-V core with associated compiler tools for efficient deployment. The flow leverages (1) Apache TVM for translating high-level Python-based DNN models into optimized C code, (2) Synopsys ASIP Designer for identifying compute-intensive kernels, modeling, and generating a custom RISC-V and (3) Xilinx Vivado for FPGA implementation. Beyond a model-class specific RISC-V, our approach produces an optimized bare-metal C implementation, eliminating the need for an OS or extensive software dependencies. Unlike conventional deployment pipelines relying on TensorFlow/PyTorch runtimes, our solution enables seamless execution in highly resource-constrained environments. We evaluated the flow on popular DNN models such as LeNet-5*, MobileNetV1, ResNet50, VGG16, MobileNetV2 and DenseNet121 using the Synopsys trv32p3 RISC-V core as a baseline. Results show a <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> speedup in inference and upto <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> reduction in energy per inference at a 28.23&#x0025; area overhead when implemented on an AMD Zynq UltraScale+ ZCU104 FPGA platform.