Nature computational science最新文献

The human brain is a complex spiking neural network (SNN) capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and Von Neumann bottleneck, hinder the efficiency of digital computers. In addition, SNNs are characterized by their software training complexities. Here, to this end, we propose a hardware-software co-design on a 40 nm 256 kB in-memory computing macro that physically integrates a fixed and random liquid state machine SNN encoder with trainable artificial neural network projections. We showcase the zero-shot learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83- and 393.07-fold reduction in training costs compared with state-of-the-art spiking recurrent neural network-based contrastive learning and prototypical networks, and a 23.34- and 160-fold improvement in energy efficiency compared with cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware.

Large language models (LLMs), with their remarkable generative capacities, have greatly impacted a range of fields, but they face scalability challenges due to their large parameter counts, which result in high costs for training and inference. The trend of increasing model sizes is exacerbating these challenges, particularly in terms of memory footprint, latency and energy consumption. Here we explore the deployment of 'mixture of experts' (MoEs) networks-networks that use conditional computing to keep computational demands low despite having many parameters-on three-dimensional (3D) non-volatile memory (NVM)-based analog in-memory computing (AIMC) hardware. When combined with the MoE architecture, this hardware, utilizing stacked NVM devices arranged in a crossbar array, offers a solution to the parameter-fetching bottleneck typical in traditional models deployed on conventional von-Neumann-based architectures. By simulating the deployment of MoEs on an abstract 3D AIMC system, we demonstrate that, due to their conditional compute mechanism, MoEs are inherently better suited to this hardware than conventional, dense model architectures. Our findings suggest that MoEs, in conjunction with emerging 3D NVM-based AIMC, can substantially reduce the inference costs of state-of-the-art LLMs, making them more accessible and energy-efficient.

Electronic circular dichroism (ECD) spectra contain key information about molecular chirality by discriminating the absolute configurations of chiral molecules, which is crucial in asymmetric organic synthesis and the drug industry. However, existing predictive approaches lack the consideration of ECD spectra owing to the data scarcity and the limited interpretability to achieve trustworthy prediction. Here we establish a large-scale dataset for chiral molecular ECD spectra and propose ECDFormer for accurate and interpretable ECD spectrum prediction. ECDFormer decomposes ECD spectra into peak entities, uses the QFormer architecture to learn peak properties and renders peaks into spectra. Compared with spectrum sequence prediction methods, our decoupled peak prediction approach substantially enhances both accuracy and efficiency, improving the peak symbol accuracy from 37.3% to 72.7% and decreasing the time cost from an average of 4.6 central processing unit hours to 1.5 s. Moreover, ECDFormer demonstrated its ability to capture molecular orbital information directly from spectral data using the explainable peak-decoupling approach. Furthermore, ECDFormer proved to be equally proficient at predicting various types of spectrum, including infrared and mass spectroscopies, highlighting its substantial generalization capabilities.