DeepCAM：一个完全基于CAM的推理加速器，具有可变哈希值，用于节能的深度神经网络
DeepCAM: A Fully CAM-based Inference Accelerator with Variable Hash Lengths for Energy-efficient Deep Neural Networks
摘要：
随着深度神经网络的深度和宽度不断增加以实现最先进的性能，深度学习计算已经显著增长，并且点积在总计算时间中仍然占主导地位。大多数先前的作品是建立在传统的点积加权输入求和是用来表示神经元操作。然而，另一种基于欧几里得空间中角度和幅度概念的点积的实现吸引了有限的关注。本文提出了DeepCAM，这是一种基于两个关键创新的推理加速器，旨在缓解卷积神经网络的计算时间瓶颈。第一个创新是建立在欧几里德空间中的计算上的近似点积，它可以用简单的逐位运算代替加法和乘法。第二个创新是动态大小的基于内容可寻址存储器（基于CAM）的加速器，用于执行逐位运算并以更低的计算时间加速CNN。我们在基准图像识别数据集上的实验表明，DeepCAM的速度分别比Eyeriss和Intel Skylake等传统CPU快523倍和3498倍。此外，与Eyeriss相比，我们的DeepCAM方法所消耗的能量少2.16倍至109倍。
英文摘要：
With ever increasing depth and width in deep neural networks to achieve state-of-the-art performance, deep learning computation has significantly grown, and dot-products remain dominant in overall computation time. Most prior works are built on conventional dot-product where weighted input summation is used to represent the neuron operation. However, another implementation of dot-product based on the notion of angles and magnitudes in the Euclidean space has attracted limited attention. This paper proposes DeepCAM, an inference accelerator built on two critical innovations to alleviate the computation time bottleneck of convolutional neural networks. The first innovation is an approximate dot-product built on computations in the Euclidean space that can replace addition and multiplication with simple bit-wise operations. The second innovation is a dynamic size content addressable memory-based (CAM-based) accelerator to perform bit-wise operations and accelerate the CNNs with a lower computation time. Our experiments on benchmark image recognition datasets demonstrate that DeepCAM is up to 523x and 3498x faster than Eyeriss and traditional CPUs like Intel Skylake, respectively. Furthermore, the energy consumed by our DeepCAM approach is 2.16x to 109x less compared to Eyeriss.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
