IBM TrueNorth chip uses digital spikes to perform neuromorphic computing and
achieves ultrahigh execution parallelism and power efficiency. However, in
TrueNorth chip, low quantization resolution of the synaptic weights and spikes
significantly limits the inference (e.g., classification) accuracy of the
deployed neural network model. Existing workaround, i.e., averaging the results
over multiple copies instantiated in spatial and temporal domains, rapidly
exhausts the hardware resources and slows down the computation. In this work,
we propose a novel learning method on TrueNorth platform that constrains the
random variance of each computation copy and reduces the number of needed
copies. Compared to the existing learning method, our method can achieve up to
68.8% reduction of the required neuro-synaptic cores or 6.5X speedup, with even
slightly improved inference accuracy.

本文介绍了一种在 TrueNorth 平台上的学习方法，通过约束每个计算副本的随机变量并减少所需的副本数量，可以实现更高的执行效率和更准确的推理，最多可将所需的神经 - 突触核心减少 68.8％或加速 6.5 倍，推理精度略有提高。

TrueNorth 芯片推理准确性、核心占用和性能协同优化的新学习方法

A New Learning Method for Inference Accuracy, Core Occupation, and  Performance Co-optimization on TrueNorth Chip

We present a model for compositional distributional semantics related to the
framework of Coecke et al. (2010), and emulating formal semantics by
representing functions as tensors and arguments as vectors. We introduce a new
learning method for tensors, generalising the approach of Baroni and Zamparelli
(2010). We evaluate it on two benchmark data sets, and find it to outperform
existing leading methods. We argue in our analysis that the nature of this
learning method also renders it suitable for solving more subtle problems
compositional distributional models might face.

介绍一种基于张量及向量的学习方法，并在数据集上进行了评估，表现超过了现有的先进方法，同时也适用于解决组合分布式模型可能遇到的更微妙问题。