{"ID":2829409,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.12847","arxiv_id":"2512.12847","title":"HaShiFlex: A High-Throughput Hardened Shifter DNN Accelerator with Fine-Tuning Flexibility","abstract":"We introduce a high-throughput neural network accelerator that embeds most network layers directly in hardware, minimizing data transfer and memory usage while preserving a degree of flexibility via a small neural processing unit for the final classification layer. By leveraging power-of-two (Po2) quantization for weights, we replace multiplications with simple rewiring, effectively reducing each convolution to a series of additions. This streamlined approach offers high-throughput, energy-efficient processing, making it highly suitable for applications where model parameters remain stable, such as continuous sensing tasks at the edge or large-scale data center deployments. Furthermore, by including a strategically chosen reprogrammable final layer, our design achieves high throughput without sacrificing fine-tuning capabilities. We implement this accelerator in a 7nm ASIC flow using MobileNetV2 as a baseline and report throughput, area, accuracy, and sensitivity to quantization and pruning - demonstrating both the advantages and potential trade-offs of the proposed architecture. We find that for MobileNetV2, we can improve inference throughput by 20x over fully programmable GPUs, processing 1.21 million images per second through a full forward pass while retaining fine-tuning flexibility. If absolutely no post-deployment fine tuning is required, this advantage increases to 67x at 4 million images per second.","short_abstract":"We introduce a high-throughput neural network accelerator that embeds most network layers directly in hardware, minimizing data transfer and memory usage while preserving a degree of flexibility via a small neural processing unit for the final classification layer. By leveraging power-of-two (Po2) quantization for weig...","url_abs":"https://arxiv.org/abs/2512.12847","url_pdf":"https://arxiv.org/pdf/2512.12847v1","authors":"[\"Jonathan Herbst\",\"Michael Pellauer\",\"Sherief Reda\"]","published":"2025-12-14T21:22:33Z","proceeding":"cs.AR","tasks":"[\"cs.AR\",\"cs.LG\"]","methods":"[]","has_code":false}