# FlexNN: A Dataflow-aware Flexible Deep Learning Accelerator for Energy-Efficient Edge Devices

arXiv: https://arxiv.org/abs/2403.09026 · HTML: https://mlsanity.com/paper/2403.09026/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2024.

## Built on by (most cited first, 9 of 9)

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- [Accelerating LLM Inference with Flexible N:M Sparsity via A Fully Digital Compute-in-Memory Accelerator](https://mlsanity.com/paper/2504.14365/) (2025)
- [GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing Units](https://mlsanity.com/paper/2502.06921/) (2025)
- [StruM: Structured Mixed Precision for Efficient Deep Learning Hardware Codesign](https://mlsanity.com/paper/2501.18953/) (2025)
- [Evaluating the Energy Efficiency of NPU-Accelerated Machine Learning Inference on Embedded Microcontrollers](https://mlsanity.com/paper/2509.17533/) (2025)
- [SPARQLe: Sub-Precision Activation Representation for Quantized LLM Inference](https://mlsanity.com/paper/2606.00365/) (2026)
- [KATANA: A Fast, Low-Power Mapping of Kalman Filters onto Edge NPUs for Real-Time Tracking](https://mlsanity.com/paper/2606.14992/) (2026)
- [Harvesting AI Computation at the Edge via Generic Approximation](https://mlsanity.com/paper/2606.29518/) (2026)

## Abstract (arXiv)

This paper introduces FlexNN, a Flexible Neural Network accelerator, which adopts agile design principles to enable versatile dataflows, enhancing energy efficiency. Unlike conventional convolutional neural network accelerator architectures that adhere to fixed dataflows (such as input, weight, output, or row stationary) for transferring activations and weights between storage and compute units, our design revolutionizes by enabling adaptable dataflows of any type through software configurable descriptors. Considering that data movement costs considerably outweigh compute costs from an energy perspective, the flexibility in dataflow allows us to optimize the movement per layer for minimal data transfer and energy consumption, a capability unattainable in fixed dataflow architectures. To further enhance throughput and reduce energy consumption in the FlexNN architecture, we propose a novel sparsity-based acceleration logic that utilizes fine-grained sparsity in both the activation and weight tensors to bypass redundant computations, thus optimizing the convolution engine within the hardware accelerator. Extensive experimental results underscore a significant enhancement in the performance and energy efficiency of FlexNN relative to existing DNN accelerators.