HaShiFlex: A High-Throughput Hardened Shifter DNN Accelerator with Fine-Tuning Flexibility

• URL: http://arxiv.org/abs/2512.12847v1
• Date: Sun, 14 Dec 2025 21:22:33 GMT
• Title: HaShiFlex: A High-Throughput Hardened Shifter DNN Accelerator with Fine-Tuning Flexibility
• Authors: Jonathan Herbst, Michael Pellauer, Sherief Reda,
• Abstract summary: We introduce a neural network accelerator that embeds most network layers directly in hardware.<n>We minimize data transfer and memory usage while preserving a degree of flexibility via a small neural processing unit for the final classification layer.
• Score: 3.443106745717184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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