论文标题
基于内容的图像检索的神经形态计算
Neuromorphic Computing for Content-based Image Retrieval
论文作者
论文摘要
神经形态计算通过模拟尖峰神经网络模仿大脑的神经活动。在众多机器学习任务中，预计神经形态芯片将在成本和功率效率方面提供出色的解决方案。在这里，我们探讨了Intel开发的神经形态计算芯片Loihi的应用，用于图像检索的计算机视觉任务。我们评估了使用深度学习嵌入在基于内容的视觉搜索和推荐系统中至关重要的功能和性能指标。我们的结果表明，与ARM Cortex-A72 CPU相比，神经形态溶液的能效高约2.5倍，与NVIDIA T4 GPU相比，通过轻量级卷积神经网络推断而无需保持相同的匹配准确度，而与NVIDIA T4 GPU相比，能源效率高12.5倍。该研究验证了低功率图像检索中神经形态计算的潜力，作为现有冯·诺伊曼体系结构的互补范式。
Neuromorphic computing mimics the neural activity of the brain through emulating spiking neural networks. In numerous machine learning tasks, neuromorphic chips are expected to provide superior solutions in terms of cost and power efficiency. Here, we explore the application of Loihi, a neuromorphic computing chip developed by Intel, for the computer vision task of image retrieval. We evaluated the functionalities and the performance metrics that are critical in content-based visual search and recommender systems using deep-learning embeddings. Our results show that the neuromorphic solution is about 2.5 times more energy-efficient compared with an ARM Cortex-A72 CPU and 12.5 times more energy-efficient compared with NVIDIA T4 GPU for inference by a lightweight convolutional neural network without batching while maintaining the same level of matching accuracy. The study validates the potential of neuromorphic computing in low-power image retrieval, as a complementary paradigm to the existing von Neumann architectures.
