BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Ultrafast Nanoscale Phase-Change Memory Enabled By Single-Pulse Conditioning

We describe how the crystallization kinetics of a suite of phase-change systems can be controlled by using a single-shot treatment via “initial crystallization” effects. Ultrarapid and highly stable phase-change structures (with excellent characteristics), viz. conventional and sub-10 nm sized cells (400 ps switching and 368 K for 10 year data retention), stackable cells (900 ps switching and 1 × 10⁶ cycles for similar “switching-on” voltages), and multilevel configurations (800 ps switching and resistance-drift power-law coefficients <0.11) have been demonstrated. Material measurements and thermal calculations also reveal the origin of the pretreatment-assisted increase in crystallization rates and the thermal diffusion in chalcogenide structures, respectively

Design of a Nanoscale, CMOS-Integrable, Thermal-Guiding Structure for Boolean-Logic and Neuromorphic Computation

One of the requirements for achieving faster CMOS electronics is to mitigate the unacceptably large chip areas required to steer heat away from or, more recently, toward the critical nodes of state-of-the-art devices. Thermal-guiding (TG) structures can efficiently direct heat by “meta-materials” engineering; however, some key aspects of the behavior of these systems are not fully understood. Here, we demonstrate control of the thermal-diffusion properties of TG structures by using nanometer-scale, CMOS-integrable, graphene-on-silica stacked materials through finite-element-methods simulations. It has been shown that it is possible to implement novel, controllable, <i>thermally based</i> Boolean-logic and spike-timing-dependent plasticity operations for advanced (neuromorphic) computing applications using such thermal-guide architectures

Compositionally matched nitrogen-doped Ge2Sb2Te 5/Ge2Sb2Te5 superlattice-like structures for phase change random access memory

10.1063/1.4823551Applied Physics Letters10313-APPL

Compositionally matched nitrogen-doped Ge2Sb2Te5/Ge2Sb2Te5 superlattice-like structures for phase change random access memory

<p>A compositionally matched superlattice-like (SLL) structure comprised of Ge2Sb2Te5 (GST) and nitrogen-doped GST (N-GST) was developed to achieve both low current and high endurancePhase Change Random Access Memory (PCRAM). N-GST/GST SLL PCRAM devices demonstrated ∼37% current reduction compared to single layered GST PCRAM and significantly higher write/erase endurances of ∼10⁸ compared to ∼10⁶ for GeTe/Sb2Te3 SLL devices. The improvements in endurance are attributed to the compositionally matched N-GST/GST material combination that lowers the diffusion gradient between the layers and the lower crystallization-induced stress in the SLL as revealed by micro-cantilever stress measurements.</p