论文标题
脑启发的低维计算分类器，用于推断微小设备
A Brain-Inspired Low-Dimensional Computing Classifier for Inference on Tiny Devices
论文作者
论文摘要
通过模仿大脑样的认知和利用并行性，高维计算（HDC）分类器已成为一种轻巧的框架，以实现有效的智障推断。尽管如此，它们具有两个基本缺点，启发式训练过程和超高维度，这会导致次优的推理准确性和大型模型大小，超出了具有严格资源限制的微型设备的能力。在本文中，我们解决了这些基本缺点，并提出了低维计算（LDC）替代方案。具体而言，通过将我们的LDC分类器映射到同等的神经网络中，我们使用原则性的培训方法来优化我们的模型。最重要的是，我们可以通过数量级成功地降低现有HDC模型的超高维度（例如8000 vs. 4/64），同时提高推理精度。我们运行实验来评估我们的LDC分类器，通过考虑针对微型设备的推断，并在FPGA平台上实现不同模型以进行加速。结果凸显了我们的LDC分类器比现有的脑启发的HDC型号具有压倒性的优势，并且特别适合推断小型设备。
By mimicking brain-like cognition and exploiting parallelism, hyperdimensional computing (HDC) classifiers have been emerging as a lightweight framework to achieve efficient on-device inference. Nonetheless, they have two fundamental drawbacks, heuristic training process and ultra-high dimension, which result in sub-optimal inference accuracy and large model sizes beyond the capability of tiny devices with stringent resource constraints. In this paper, we address these fundamental drawbacks and propose a low-dimensional computing (LDC) alternative. Specifically, by mapping our LDC classifier into an equivalent neural network, we optimize our model using a principled training approach. Most importantly, we can improve the inference accuracy while successfully reducing the ultra-high dimension of existing HDC models by orders of magnitude (e.g., 8000 vs. 4/64). We run experiments to evaluate our LDC classifier by considering different datasets for inference on tiny devices, and also implement different models on an FPGA platform for acceleration. The results highlight that our LDC classifier offers an overwhelming advantage over the existing brain-inspired HDC models and is particularly suitable for inference on tiny devices.
