通过马尔可夫链蒙特卡罗采样利用固有电阻记忆可变性的原位学习
In-situ learning harnessing intrinsic resistive memory variability through Markov Chain Monte Carlo Sampling
摘要：
电阻式存储器技术有望成为解锁下一代智能存内计算系统的关键组件，这些系统可以在边缘进行本地操作和学习。然而，当前的内存中机器学习方法通常集中在模型和算法的实现上，这些模型和算法不能与电阻存储器的真实物理特性相协调。因此，这些属性，特别是循环间电导变化，被视为需要缓解的非理想性。相比之下，我们通过选择更合适的机器学习模型和算法来拥抱这些属性。我们在16，384个设备的制造阵列中实现马尔可夫链蒙特卡罗采样算法，配置为贝叶斯机器学习模型。该算法是实现原位，通过利用设备作为随机变量的角度来看，他们的周期到周期的电导变化。我们训练实验的内存阵列执行说明性的监督学习任务，以及恶性乳腺组织识别任务，达到96.3%的准确率。然后，使用阵列级测量校准的电阻存储器的行为模型，我们将相同的方法应用于Cartpole强化学习任务。在所有情况下，我们提出的方法优于基于软件的神经网络模型实现使用相同数量的存储器元件。这一结果为内存中机器学习的新路径奠定了基础，与电阻式存储器技术的真正特性兼容，可以为智能边缘计算系统带来本地化的学习能力。
英文摘要：
Resistive memory technologies promise to be a key component in unlocking the next generation of intelligent in-memory computing systems that can act and learn locally at the edge. However, current approaches to in-memory machine learning focus often on the implementation of models and algorithms which cannot be reconciled with the true, physical properties of resistive memory. Consequently, these properties, in particular cycle-to-cycle conductance variability, are considered as non-idealities that require mitigation. Here by contrast, we embrace these properties by selecting a more appropriate machine learning model and algorithm. We implement a Markov Chain Monte Carlo sampling algorithm within a fabricated array of 16,384 devices, configured as a Bayesian machine learning model. The algorithm is realised in-situ, by exploiting the devices as random variables from the perspective of their cycle-to-cycle conductance variability. We train experimentally the memory array to perform an illustrative supervised learning task as well as a malignant breast tissue recognition task, achieving an accuracy of 96.3%. Then, using a behavioural model of resistive memory calibrated on array level measurements, we apply the same approach to the Cartpole reinforcement learning task. In all cases our proposed approach outperformed software-based neural network models realised using an equivalent number of memory elements. This result lays a foundation for a new path in-memory machine learning, compatible with the true properties of resistive memory technologies, that can bring localised learning capabilities to intelligent edge computing systems.
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