arXiv subjects

Dengfeng Wang

Publications and source records attributed to Dengfeng Wang.

3 recordsLinked to original sources

arXiv2026

PC2IM: An Efficient In-Memory Computing Accelerator for 3D Point Cloud

3D point cloud neural networks have significantly enhanced the perceptual capabilities of resource-limited mobile intelligent systems. However, despite the transformative impact, the point cloud algorithm suffers from substantial memory access during data preprocessing and imposes a burdensome workload on feature computing, resulting in high energy consumption and latency. In this paper, an efficient SRAM-based computing-in-memory (SRAM-CIM) accelerator (PC2IM), is proposed to alleviate memory access bottlenecks in point-based 3D point cloud networks. A data preprocessing module driven by the customized CIM engines is proposed and incorporated into a memory-efficient data flow. Specifically, an approximate distance SRAM-CIM (APD-CIM) is introduced to eliminate the repetitive on-chip memory access for point clouds that are spatially partitioned by the median and reduce the volume of temporary distance data. Building on the APD-CIM, a two-level Ping-Pong-MAX Content Addressable Memory (Ping-Pong-MAX CAM) is introduced to adaptively update temporary distances and perform in-situ search for the maximum, further reducing memory access. Additionally, an efficient CIM-based feature computing engine, named split-concatenate SRAM-CIM, is presented to minimize computation latency in multi-layer perceptron with high-precision input, while maintaining high area and energy efficiency. Experiment results show that the proposed PC2IM demonstrates 1.5x speedup and 2.7x enhanced energy efficiency compared to state-of-the-art point cloud accelerator. Moreover, PC2IM achieves 3.5x speedup and 1518.9x enhanced energy efficiency compared to GPU implementations.

cs.AR↗

arXiv2025

Non-Hermitian higher-order topological insulators enabled by altermagnet engineering

We show that proximity to an altermagnet provides an efficient route to engineering non-Hermitian higher-order topological phases. The proximity-induced altermagnetic order gaps the edge states of a topological insulator, thereby driving a transition from a first-order to a second-order topological phase. When combined with nonreciprocal hopping, the system exhibits both the non-Hermitian skin effect and a hybrid skin-topological effect, whereby first-order edge states and second-order corner states accumulate at selected corners of the lattice. We demonstrate that the spectral winding number of the edge states under cylindrical geometry dictates this corner localization and can be reversed by tuning the altermagnetic order. Consequently, both edge and corner states become directionally controllable. Our results establish altermagnets as a versatile platform for realizing and tuning skin-topological phenomena in non-Hermitian higher-order topological systems.

cond-mat.mes-hall↗

arXiv2024

TL-nvSRAM-CIM: Ultra-High-Density Three-Level ReRAM-Assisted Computing-in-nvSRAM with DC-Power Free Restore and Ternary MAC Operations

Accommodating all the weights on-chip for large-scale NNs remains a great challenge for SRAM based computing-in-memory (SRAM-CIM) with limited on-chip capacity. Previous non-volatile SRAM-CIM (nvSRAM-CIM) addresses this issue by integrating high-density single-level ReRAMs on the top of high-efficiency SRAM-CIM for weight storage to eliminate the off-chip memory access. However, previous SL-nvSRAM-CIM suffers from poor scalability for an increased number of SL-ReRAMs and limited computing efficiency. To overcome these challenges, this work proposes an ultra-high-density three-level ReRAMs-assisted computing-in-nonvolatile-SRAM (TL-nvSRAM-CIM) scheme for large NN models. The clustered n-selector-n-ReRAM (cluster-nSnRs) is employed for reliable weight-restore with eliminated DC power. Furthermore, a ternary SRAM-CIM mechanism with differential computing scheme is proposed for energy-efficient ternary MAC operations while preserving high NN accuracy. The proposed TL-nvSRAM-CIM achieves 7.8x higher storage density, compared with the state-of-art works. Moreover, TL-nvSRAM-CIM shows up to 2.9x and 1.9x enhanced energy-efficiency, respectively, compared to the baseline designs of SRAM-CIM and ReRAM-CIM, respectively.

cs.AR↗