Search

AbstractFrom open-source software for collection management to open access initiatives for public reuse, the museum community is experiencing an ongoing transformation in what the concept of openness truly means. Adopting the social construction of technology as a theoretical framework and analytical method, this study investigates the changing attitudes and practices among museum professionals, especially how the intellectual precepts of the “new museology” have influenced the conceptualisation and operationalisation of openness. Methodologically, this study explores the archive of the Museums and the Web conference and conducts a qualitative content analysis of papers presented between 1997 and 2020 related to the topic of openness. By identifying and comparing how these papers discussed open-oriented practices, this study demonstrates how museum professionals have socially constructed the changing meanings of openness in the past twenty years and provides empirical evidence about how the new museology discourse has been realised in practice.

In this work, a non-idealities aware software-hardware co-design framework for deep neural network (DNN) implemented on memristive crossbar is presented. The device level non-ideal factors such as device conductance variation, nonuniform quantization levels, device-to-device variation and programming failure probability are included in the model. At array level, the impact of line resistance and sneak path are considered using a new fast and accurate line resistance estimation model. The non-linearity and offset of the peripheral circuits are also considered. By incorporating these factors into a unified DNN training process, the neural network performance can be evaluated holistically. Furthermore, the proposed training process can effectively mitigate the impact of these non-idealities and reduce the inference accuracy degradations. Implemented in a hybrid fashion of Python and PyTorch, the proposed framework is evaluated with a simplified 5-layer VGG network implemented on measured $128\times 128$ RRAM array with 3-level weight resolution. For CIFAR-10 tasks, 83% inference accuracy is achieved with less than 3% accuracy drop compared to the ideal model.

This paper presents a parasitic-aware modeling method for fast and accurate simulation of Processing-in-Memory (PIM) neural network (NN) implemented in resistive memristor crossbar array. This work proposed an efficient and accurate crossbar line resistance estimation model named $\gamma $ -compact model, and the associated NN training scheme that takes the impact of line resistance into consideration with time complexity of O(mn) for a $\text{m}\times \text{n}$ resistive crossbar. The impact of the crossbar array parasitics to the vector-matrix multiplication (VMM) computation accuracy and multi-layer PIM NN inference accuracy are analyzed in detail. The advantage of the proposed model is demonstrated by a multilayer perceptron (MLP) of size $784\times 128\times 10$ implemented with resistive random access memory (RRAM) crossbar arrays for MNIST hand-written digits classification. The proposed method reduced the VMM computation error by 186 and 17 times compared to uncompensated method and the state-of-art compensation method, respectively. Maximum 98.1% inference accuracy is achieved with only 0.17% degradation compared to the ideal model.