Spiking neural networks (SNNs) promise ultra-low-power applications by
exploiting temporal and spatial sparsity. The number of binary activations,
called spikes, is proportional to the power consumed when executed on
neuromorphic hardware. Training such SNNs using backpropagation through time
for vision tasks that rely mainly on spatial features is computationally
costly. Training a stateless artificial neural network (ANN) to then convert
the weights to an SNN is a straightforward alternative when it comes to image
recognition datasets. Most conversion methods rely on rate coding in the SNN to
represent ANN activation, which uses enormous amounts of spikes and, therefore,
energy to encode information. Recently, temporal conversion methods have shown
promising results requiring significantly fewer spikes per neuron, but
sometimes complex neuron models. We propose a temporal ANN-to-SNN conversion
method, which we call Quartz, that is based on the time to first spike (TTFS).
Quartz achieves high classification accuracy and can be easily implemented on
neuromorphic hardware while using the least amount of synaptic operations and
memory accesses. It incurs a cost of two additional synapses per neuron
compared to previous temporal conversion methods, which are readily available
on neuromorphic hardware. We benchmark Quartz on MNIST, CIFAR10, and ImageNet
in simulation to show the benefits of our method and follow up with an
implementation on Loihi, a neuromorphic chip by Intel. We provide evidence that
temporal coding has advantages in terms of power consumption, throughput, and
latency for similar classification accuracy. Our code and models are publicly
available.

基于时间编码的 ANN 到 SNN 转换方法 Quartz 在神经形态硬件上实现了高分类准确性，通过时间编码在功耗、吞吐量和延迟方面具有优势。

神经形态硬件上的超低功耗图像分类

Ultra-low-power Image Classification on Neuromorphic Hardware

We introduce the first open-domain dataset, called QuaRTz, for reasoning
about textual qualitative relationships. QuaRTz contains general qualitative
statements, e.g., "A sunscreen with a higher SPF protects the skin longer.",
twinned with 3864 crowdsourced situated questions, e.g., "Billy is wearing
sunscreen with a lower SPF than Lucy. Who will be best protected from the
sun?", plus annotations of the properties being compared. Unlike previous
datasets, the general knowledge is textual and not tied to a fixed set of
relationships, and tests a system's ability to comprehend and apply textual
qualitative knowledge in a novel setting. We find state-of-the-art results are
substantially (20%) below human performance, presenting an open challenge to
the NLP community.

介绍了第一个开放领域的定性关系数据集 QuaRTz，用于推理文本定性关系。QuaRTz 包含一般的定性陈述，结合 3864 个众包定位问题，对应的是类似 “Billy 涂有比 Lucy 更低 SPF 的防晒霜。谁的皮肤最好受保护？” 的问题，以及被比较对象的注释。与以往的数据集不同，一般知识是文本而不是固定的关系集，并且在新颖环境中测试系统理解和应用文本定性知识的能力。实验结果表明，最先进的结果比人类表现低得多（20％），这是自然语言处理社区的一个开放挑战。

QuaRTz：一份定性关系问题开放领域数据集

QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

In this work we develop a new algorithm for regularized empirical risk
minimization. Our method extends recent techniques of Shalev-Shwartz [02/2015],
which enable a dual-free analysis of SDCA, to arbitrary mini-batching schemes.
Moreover, our method is able to better utilize the information in the data
defining the ERM problem. For convex loss functions, our complexity results
match those of QUARTZ, which is a primal-dual method also allowing for
arbitrary mini-batching schemes. The advantage of a dual-free analysis comes
from the fact that it guarantees convergence even for non-convex loss
functions, as long as the average loss is convex. We illustrate through
experiments the utility of being able to design arbitrary mini-batching
schemes.

本文提出了一种新算法用于规则经验风险最小化问题，可以使非凸损失函数也能收敛，达到与 QUARTZ 相同的复杂度，同时也能更好地利用数据信息，实现任意小批量的设计。