We present a fully memristive spiking neural network (MSNN) consisting of
physically-realizable memristive neurons and memristive synapses to implement
an unsupervised Spiking Time Dependent Plasticity (STDP) learning rule. The
system is fully memristive in that both neuronal and synaptic dynamics can be
realized by using memristors. The neuron is implemented using the SPICE-level
memristive integrate-and-fire (MIF) model, which consists of a minimal number
of circuit elements necessary to achieve distinct depolarization,
hyperpolarization, and repolarization voltage waveforms. The proposed MSNN
uniquely implements STDP learning by using cumulative weight changes in
memristive synapses from the voltage waveform changes across the synapses,
which arise from the presynaptic and postsynaptic spiking voltage signals
during the training process. Two types of MSNN architectures are investigated:
1) a biologically plausible memory retrieval system, and 2) a multi-class
classification system. Our circuit simulation results verify the MSNN's
unsupervised learning efficacy by replicating biological memory retrieval
mechanisms, and achieving 97.5% accuracy in a 4-pattern recognition problem in
a large scale discriminative MSNN.

本研究基于 memristive neuron 和 memristive synapse 提出了一个完全基于 memristor 的脉冲神经网络 (MSNN)，并采用无监督的 Spiking Time Dependent Plasticity (STDP) 学习规则。我们通过电路模拟验证了 MSNN 的学习效果，并实现了两种类型的 MSNN 结构：一种是生物学合理的记忆检索系统，另一种是用于多类别分类。

具有无监督学习的全脉冲记忆电阻网络

A Fully Memristive Spiking Neural Network with Unsupervised Learning

Analog IC design relies on human experts to search for parameters that
satisfy circuit specifications with their experience and intuitions, which is
highly labor intensive, time consuming and suboptimal. Machine learning is a
promising tool to automate this process. However, supervised learning is
difficult for this task due to the low availability of training data: 1)
Circuit simulation is slow, thus generating large-scale dataset is
time-consuming; 2) Most circuit designs are propitiatory IPs within individual
IC companies, making it expensive to collect large-scale datasets. We propose
Learning to Design Circuits (L2DC) to leverage reinforcement learning that
learns to efficiently generate new circuits data and to optimize circuits. We
fix the schematic, and optimize the parameters of the transistors automatically
by training an RL agent with no prior knowledge about optimizing circuits.
After iteratively getting observations, generating a new set of transistor
parameters, getting a reward, and adjusting the model, L2DC is able to optimize
circuits. We evaluate L2DC on two transimpedance amplifiers. Trained for a day,
our RL agent can achieve comparable or better performance than human experts
trained for a quarter. It first learns to meet hard-constraints (eg. gain,
bandwidth), and then learns to optimize good-to-have targets (eg. area, power).
Compared with grid search-aided human design, L2DC can achieve
$\mathbf{250}\boldsymbol{\times}$ higher sample efficiency with comparable
performance. Under the same runtime constraint, the performance of L2DC is also
better than Bayesian Optimization.

L2DC 是一种基于强化学习的电路设计方法，通过利用无先验知识的 RL agent 优化电路参数并自动生成新电路数据，实现对电路的自动优化，相比于传统的人工设计或贝叶斯优化方法，L2DC 在样本效率和性能上都具有更优的表现。