Edge State Transport of Separately Contacted Bilayer Systems in the Fractional Quantum Hall Regime

Hall and diagonal resistances of bilayer fractional quantum Hall systems are discussed theoretically. The bilayers have electrodes attached separately to each layer. They are assumed to be coupled weakly by interlayer tunneling, while the interlayer Coulomb interaction is negligibly small. It is shown that source-drain voltage dependence of the resistances reflects the Luttinger liquid parameter of the edge state.Comment: 3 pages with 2 eps figure, Revtex, contributed paper to EP2DS-1

OSA-HCIM: On-The-Fly Saliency-Aware Hybrid SRAM CIM with Dynamic Precision Configuration

Computing-in-Memory (CIM) has shown great potential for enhancing efficiency and performance for deep neural networks (DNNs). However, the lack of flexibility in CIM leads to an unnecessary expenditure of computational resources on less critical operations, and a diminished Signal-to-Noise Ratio (SNR) when handling more complex tasks, significantly hindering the overall performance. Hence, we focus on the integration of CIM with Saliency-Aware Computing -- a paradigm that dynamically tailors computing precision based on the importance of each input. We propose On-the-fly Saliency-Aware Hybrid CIM (OSA-HCIM) offering three primary contributions: (1) On-the-fly Saliency-Aware (OSA) precision configuration scheme, which dynamically sets the precision of each MAC operation based on its saliency, (2) Hybrid CIM Array (HCIMA), which enables simultaneous operation of digital-domain CIM (DCIM) and analog-domain CIM (ACIM) via split-port 6T SRAM, and (3) an integrated framework combining OSA and HCIMA to fulfill diverse accuracy and power demands. Implemented on a 65nm CMOS process, OSA-HCIM demonstrates an exceptional balance between accuracy and resource utilization. Notably, it is the first CIM design to incorporate a dynamic digital-to-analog boundary, providing unprecedented flexibility for saliency-aware computing. OSA-HCIM achieves a 1.95x enhancement in energy efficiency, while maintaining minimal accuracy loss compared to DCIM when tested on CIFAR100 dataset