arXiv subjects

Saitao Zhang

Publications and source records attributed to Saitao Zhang.

3 recordsLinked to original sources

arXiv2026

A neuromorphic vision system for open-world visual intelligence

Time-efficient and robust visual intelligence remains a critical challenge in unstructured open-world environments, yet current approaches often rely on computationally intensive neural architectures or task-specific sensors with limited versatility. Inspired by biological vision and information bottleneck theory, we report a neuromorphic vision system that performs task-oriented visual intelligence through an information distillation strategy (named as task traction mechanism) implemented on hardware. The system integrates a polarization-sensitive imager with a resistive random-access memory (RRAM) array to progressively distill task-relevant information via light field selection, region of interest extraction, and target anticipation. The neuromorphic vision system conducts visual tasks within an execution time of 193 μs. Evaluation across eight challenging open-world scenarios shows accuracy improvements of 25.54%, 37.73%, and 36.10% for object tracking, object segmentation, and trajectory prediction, respectively, together with an average 30.6-fold reduction in latency relative to state-of-the-art solutions.

eess.IV↗

arXiv2026

SimAMC: A Fast and Accurate Simulator for Resistive Memory-Based Analog Matrix Computing with Non-Idealities

Analog matrix computing (AMC) circuits leverage resistive memory arrays to perform matrix operations in a massively parallel manner, providing an efficient approach for accelerating data-intensive tasks. However, hardware non-idealities severely impact computational accuracy, making early-stage simulation vital for reliable performance estimation and design optimization. While open-loop circuits for matrix-vector multiplication are well-studied, closed-loop AMC circuits, which solve matrix equations, are computationally more complex and substantially more sensitive to non-idealities, complicating their simulation. In this work, we present SimAMC, a simulator for resistive memory-based closed-loop AMC circuits. SimAMC is capable of modeling matrix inversion and eigenvector solving in the presence of key non-idealities, including device programming error, data conversion error, thermal noise, operational amplifier input offset, and interconnect resistance. For real-valued matrix computing circuits, an alternating iterative algorithm is designed. SimAMC's effectiveness is validated through comparison with SPICE, showing excellent agreement while also demonstrating a speedup of several orders of magnitude.

cs.ET↗

arXiv2025

First Demonstration of Second-order Training of Deep Neural Networks with In-memory Analog Matrix Computing

Second-order optimization methods, which leverage curvature information, offer faster and more stable convergence than first-order methods such as stochastic gradient descent (SGD) and Adam. However, their practical adoption is hindered by the prohibitively high cost of inverting the second-order information matrix, particularly in large-scale neural network training. Here, we present the first demonstration of a second-order optimizer powered by in-memory analog matrix computing (AMC) using resistive random-access memory (RRAM), which performs matrix inversion (INV) in a single step. We validate the optimizer by training a two-layer convolutional neural network (CNN) for handwritten letter classification, achieving 26% and 61% fewer training epochs than SGD with momentum and Adam, respectively. On a larger task using the same second-order method, our system delivers a 5.88x improvement in throughput and a 6.9x gain in energy efficiency compared to state-of-the-art digital processors. These results demonstrate the feasibility and effectiveness of AMC circuits for second-order neural network training, opening a new path toward energy-efficient AI acceleration.

cs.ET↗