Machine learning has emerged as the dominant tool for implementing complex
cognitive tasks that require supervised, unsupervised, and reinforcement
learning. While the resulting machines have demonstrated in some cases even
super-human performance, their energy consumption has often proved to be
prohibitive in the absence of costly super-computers. Most state-of-the-art
machine learning solutions are based on memory-less models of neurons. This is
unlike the neurons in the human brain, which encode and process information
using temporal information in spike events. The different computing principles
underlying biological neurons and how they combine together to efficiently
process information is believed to be a key factor behind their superior
efficiency compared to current machine learning systems. Inspired by the
time-encoding mechanism used by the brain, third generation spiking neural
networks (SNNs) are being studied for building a new class of information
processing engines.
Modern computing systems based on the von Neumann architecture, however, are
ill-suited for efficiently implementing SNNs, since their performance is
limited by the need to constantly shuttle data between physically separated
logic and memory units. Hence, novel computational architectures that address
the von Neumann bottleneck are necessary in order to build systems that can
implement SNNs with low energy budgets. In this paper, we review some of the
architectural and system level design aspects involved in developing a new
class of brain-inspired information processing engines that mimic the
time-based information encoding and processing aspects of the brain.

本文回顾了构建新一类仿效人脑以时间编码和处理信息的信息处理引擎所涉及的一些体系结构和系统级设计方面，以解决能效问题。

低功耗神经形态处理硬件

Low-Power Neuromorphic Hardware for Signal Processing Applications

With the success of new computational architectures for visual processing,
such as convolutional neural networks (CNN) and access to image databases with
millions of labeled examples (e.g., ImageNet, Places), the state of the art in
computer vision is advancing rapidly. One important factor for continued
progress is to understand the representations that are learned by the inner
layers of these deep architectures. Here we show that object detectors emerge
from training CNNs to perform scene classification. As scenes are composed of
objects, the CNN for scene classification automatically discovers meaningful
objects detectors, representative of the learned scene categories. With object
detectors emerging as a result of learning to recognize scenes, our work
demonstrates that the same network can perform both scene recognition and
object localization in a single forward-pass, without ever having been
explicitly taught the notion of objects.

本文讨论了利用卷积神经网络进行场景分类的方法，通过该方法可以自动学习场景中有意义的对象探测器，从而在单个前向传递中实现场景识别和对象定位。