Oscillation-free Quantization for Low-bit Vision Transformers

Weight oscillation is an undesirable side effect of quantization-aware training, in which quantized weights frequently jump between two quantized levels, resulting in training instability and a sub-optimal final model. We discover that the learnable scaling factor, a widely-used $\textit{de facto}$ setting in quantization aggravates weight oscillation. In this study, we investigate the connection between the learnable scaling factor and quantized weight oscillation and use ViT as a case driver to illustrate the findings and remedies. In addition, we also found that the interdependence between quantized weights in $\textit{query}$ and $\textit{key}$ of a self-attention layer makes ViT vulnerable to oscillation. We, therefore, propose three techniques accordingly: statistical weight quantization ($\rm StatsQ$) to improve quantization robustness compared to the prevalent learnable-scale-based method; confidence-guided annealing ($\rm CGA$) that freezes the weights with $\textit{high confidence}$ and calms the oscillating weights; and $\textit{query}$-$\textit{key}$ reparameterization ($\rm QKR$) to resolve the query-key intertwined oscillation and mitigate the resulting gradient misestimation. Extensive experiments demonstrate that these proposed techniques successfully abate weight oscillation and consistently achieve substantial accuracy improvement on ImageNet. Specifically, our 2-bit DeiT-T/DeiT-S algorithms outperform the previous state-of-the-art by 9.8% and 7.7%, respectively. Code and models are available at: https://github.com/nbasyl/OFQ.Comment: Proceedings of the 40 th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 202

Team Quotients, Resilience, and Performance of Software Development Projects

Past studies have examined actions and strategies that software project teams can take to reduce the negative impact of uncertainties, such as changing requirements. Software development project teams often have to be flexible to follow the pre-defined plans and strive to meet project goals. Sometimes uncertainty may go extreme to temporarily slow projects down and set project teams into reduced productivity. Project teams should be resilient to recover from the reduce productivity condition and move forward toward predefined goals. This study focuses on understanding the importance of team resilience for software project teams and exploring the antecedents of team resilience. Specifically, we investigate the impacts of intelligence and emotional quotient on team resilience capability, the extent to which project team can recover from the impediment and move forward. This is a research-in-progress work. A future empirical test plan has been discussed at the end

Mechanism of thermal field and electric field in resistive random access memory using the high/low-k side wall structure

In the Internet of things (IoT) era, low power consumption memory will be a critical issue for further device development. Among many kinds of next-generation memories, resistive random access memory (RRAM) is considered as having the most potential due to its high performance. To prevent unrecoverable hard break-down of a RRAM device, the RRAM should be collocated with a transistor for external current compliance. With decreasing device cell size, however, the operating voltage of the transistor will become smaller and smaller. Previous study has determined that the forming voltage of RRAM increases when device cell size is reduced, which is a very crucial issue especially when the device is scaled down. We have proposed a high-k sidewall spacer structure in RRAM to solve the dilemma of increasing forming voltages for device cell scaling down. Based on the COMSOL-simulated electrical field distributions in the high-k RRAM. In addition, thermal conductivity of sidewall spacer influenced resistive switching behavior. Suitable thermal conductivity of sidewall materials can enhance resistive switching behavior. Please click Additional Files below to see the full abstract

Efficient Quantization-aware Training with Adaptive Coreset Selection

The expanding model size and computation of deep neural networks (DNNs) have increased the demand for efficient model deployment methods. Quantization-aware training (QAT) is a representative model compression method to leverage redundancy in weights and activations. However, most existing QAT methods require end-to-end training on the entire dataset, which suffers from long training time and high energy costs. Coreset selection, aiming to improve data efficiency utilizing the redundancy of training data, has also been widely used for efficient training. In this work, we propose a new angle through the coreset selection to improve the training efficiency of quantization-aware training. Based on the characteristics of QAT, we propose two metrics: error vector score and disagreement score, to quantify the importance of each sample during training. Guided by these two metrics of importance, we proposed a quantization-aware adaptive coreset selection (ACS) method to select the data for the current training epoch. We evaluate our method on various networks (ResNet-18, MobileNetV2), datasets(CIFAR-100, ImageNet-1K), and under different quantization settings. Compared with previous coreset selection methods, our method significantly improves QAT performance with different dataset fractions. Our method can achieve an accuracy of 68.39% of 4-bit quantized ResNet-18 on the ImageNet-1K dataset with only a 10% subset, which has an absolute gain of 4.24% compared to the baseline.Comment: Code: https://github.com/HuangOwen/QAT-AC

LLM-FP4: 4-Bit Floating-Point Quantized Transformers

We propose LLM-FP4 for quantizing both weights and activations in large language models (LLMs) down to 4-bit floating-point values, in a post-training manner. Existing post-training quantization (PTQ) solutions are primarily integer-based and struggle with bit widths below 8 bits. Compared to integer quantization, floating-point (FP) quantization is more flexible and can better handle long-tail or bell-shaped distributions, and it has emerged as a default choice in many hardware platforms. One characteristic of FP quantization is that its performance largely depends on the choice of exponent bits and clipping range. In this regard, we construct a strong FP-PTQ baseline by searching for the optimal quantization parameters. Furthermore, we observe a high inter-channel variance and low intra-channel variance pattern in activation distributions, which adds activation quantization difficulty. We recognize this pattern to be consistent across a spectrum of transformer models designed for diverse tasks, such as LLMs, BERT, and Vision Transformer models. To tackle this, we propose per-channel activation quantization and show that these additional scaling factors can be reparameterized as exponential biases of weights, incurring a negligible cost. Our method, for the first time, can quantize both weights and activations in the LLaMA-13B to only 4-bit and achieves an average score of 63.1 on the common sense zero-shot reasoning tasks, which is only 5.8 lower than the full-precision model, significantly outperforming the previous state-of-the-art by 12.7 points. Code is available at: https://github.com/nbasyl/LLM-FP4.Comment: EMNLP 2023 Main Conferenc

General Versus Spinal Anesthesia: Which is a Risk Factor for Octogenarian Hip Fracture Repair Patients?

SummaryBackgroundMost studies have shown no difference between the two types of anesthesia administered to hip fracture patients. This study compared postoperative morbidity and mortality in octogenarian patients who received either general or spinal anesthesia for hip fracture repair.MethodsWe retrospectively analyzed the hospital records of 335 octogenarian patients who received hip fracture repair in our teaching hospital between 2002 and 2006. A total of 167 and 168 patients received general and spinal anesthesia, respectively. Morbidity, mortality, and intraoperative and preoperative variables were compared between groups.ResultsThere were no mortality differences between spinal and general anesthesia groups. However, the overall morbidity was greater in the general anesthesia group than in the spinal anesthesia group (21/167 [12.6%] vs. 9/168 [5.4%]; p = 0.02). Respiratory system-related morbidity was also higher in the general anesthesia group than in the spinal anesthesia group (11/167 [6.6%] vs. 3/168 [1.8%]; p = 0.03). Logistic regression analysis revealed two significant predictors of postoperative morbidity: anesthesia type (general; odds ratio, 2.39) and preexisting respiratory diseases (odds ratio, 3.38).ConclusionGeneral anesthesia increased the risk of postoperative morbidity in octogenarian patients after hip fracture repair, and patients with preexisting respiratory diseases were especially vulnerable. Spinal anesthesia is strongly recommended in such individuals

Understanding the Role of Knowledge Co-Production between Users and Developers in ISD Project: An Intellectual Capital Perspective

Information system development (ISD) has long been treated as that process that system developers craft an artifact to support business operation based on their special expertise. However, a significant portion of projects still have failed because the developed outcome cannot fit users’ needs. An emerging internal service concept indicates that, by treating ISD as one type of service, the requirement definition can be viewed as a co-production process in which users and developers integrate their own knowledge. By incorporating this concept into research design and taking intellectual capital perspective into account, this study proposed a model to examine the antecedents and consequences of knowledge co-production between users and developers. Data collected from 267 developers confirmed our hypotheses that knowledge co-production can benefit ISD outcomes, and common knowledge, relational capital and participative decision-making between these two parties increase the effectiveness of knowledge co-production effectively. Lastly, the implications toward academic and practitioner are also provided

Anthropomorphism of AI-based Intelligent Customer Service, and Its Affective and Behavioral Consequences

Recently, as many users turn to social media to interact with service providers, organizations apply Artificial intelligence (AI) to improve the efficiency and effectiveness of the operation. This type of customer service system is called intelligent customer service (ICS) which one of the most commonly adopted tools is chatbot. Since chatbot is AI-empowered, whether this system can effectively interact with customers and solve their problems is critical. However, the quality of ICS has received significant attention recently, and a lack of systematic study on the outcomes of anthropomorphism leaves this question unanswered in an ICS context. Based on a cognitive-affective-behavioral framework, this study attempts to understand whether anthropomorphism can promote desired behaviors (including usage and citizen-ship behaviors) through enhancing affective out-comes, such as satisfaction and identity. Data collected from 183 chatbot-ICS users, this study illustrates how anthropomorphism can increase quality, enhance satisfaction and identity. Furthermore, we also show that satisfaction and identity lead to further usage and citizenship behaviors. This highlights the importance of increasing anthropomorphism for the chatbot-ICS