UvA-DARE (Digital Academic Repository) PELSI: Power-Efficient Layer-Switched Inference

UvA-DARE (Digital Academic Repository) PELSI: Power-Efficient Layer-Switched Inference

PELSI incorporates a Genetic Algorithm to identify the near-optimal CPU-GPU layer-switched CNN inference configuration from within the large exponential design space that meets the given latency requirement most power efficiently.

UvA-DARE (Digital Academic Repository) ARM-CO-UP: ARM COoperative Utilization of Processors

UvA-DARE (Digital Academic Repository) ARM-CO-UP: ARM COoperative Utilization of Processors

The ARM-CO-UP framework is a comprehensive framework for multi-processor CNN inference that automates CNN partitioning and mapping, pipeline synchronization, processor type switching, layer-wise DVFS, and closed-source NPU integration.

Hardware-Aware Neural Network Deployment on Multi-Core in-Memory Computing Systems: A Compiler Perspective

Conventional compute-in-memory (CIM) deployment flows usually assume a fixed crossbar geometry, although DNN layers often have different channel counts and matrix shapes. The resulting shape mismatch leaves part of the array capacity unused and can increase the number of split-and-transfer operations during inference....

Fine-Grained Energy Assessment of Vision Transformer Operators on Hybrid SoC Platforms: A CPU, iGPU, and NPU Case Study

As CPUs alone are often insufficiently efficient for running Vision Transformers (ViTs), contemporary computing platforms, particularly edge systems, increasingly adopt heterogeneous architectures that combine CPUs with GPUs and Neural Processing Units (NPUs). This paper presents a detailed energy assessment of individ...

Designing and Building an FPGA Accelerator That Uses Less Energy for DNN Inference

Deep Neural Networks (DNNs) are critical to modern AI applications, yet their deployment on standard CPUs and GPUs is constrained by high power consumption and computational latency, particularly in resource-constrained edge environments. To address these limitations, this paper presents the design and implementation o...