Resistive random access memory (ReRAM)-based processing-in-memory (PIM) architectures have demonstrated great potential to accelerate Deep Neural Network (DNN) training/inference. However, the computational accuracy of analog PIM is compromised due to the non-idealities, such as the conductance variation of ReRAM cells. The impact of these non-idealities worsens as the number of concurrently activated wordlines and bitlines increases. To guarantee computational accuracy, only a limited number of wordlines and bitlines of the crossbar array can be turned on concurrently, significantly reducing the achievable parallelism of the architecture. While the constraints on parallelism limit the efficiency of the accelerators, they also provide a new opportunity for fine-grained mixed-precision quantization. To enable efficient DNN inference on practical ReRAM-based accelerators, we propose an algorithm-architecture co-design framework called \underline{B}lock-\underline{W}ise mixed-precision \underline{Q}uantization (BWQ). At the algorithm level, BWQ-A introduces a mixed-precision quantization scheme at the block level, which achieves a high weight and activation compression ratio with negligible accuracy degradation. We also present the hardware architecture design BWQ-H, which leverages the low-bit-width models achieved by BWQ-A to perform high-efficiency DNN inference on ReRAM devices. BWQ-H also adopts a novel precision-aware weight mapping method to increase the ReRAM crossbar's throughput. Our evaluation demonstrates the effectiveness of BWQ, which achieves a 6.08x speedup and a 17.47x energy saving on average compared to existing ReRAM-based architectures.
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Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition. We think the key to skeleton-based action recognition is a skeleton hanging in frames, so we focus on how the Graph Convolutional Convolution networks learn different topologies and effectively aggregate joint features in the global temporal and local temporal. In this work, we propose three Channel-wise Tolopogy Graph Convolution based on Channel-wise Topology Refinement Graph Convolution (CTR-GCN). Combining CTR-GCN with two joint cross-attention modules can capture the upper-lower body part and hand-foot relationship skeleton features. After that, to capture features of human skeletons changing in frames we design the Temporal Attention Transformers to extract skeletons effectively. The Temporal Attention Transformers can learn the temporal features of human skeleton sequences. Finally, we fuse the temporal features output scale with MLP and classification. We develop a powerful graph convolutional network named Spatial Temporal Effective Body-part Cross Attention Transformer which notably high-performance on the NTU RGB+D, NTU RGB+D 120 datasets. Our code and models are available at //github.com/maclong01/STEP-CATFormer
Semi-supervised object detection is crucial for 3D scene understanding, efficiently addressing the limitation of acquiring large-scale 3D bounding box annotations. Existing methods typically employ a teacher-student framework with pseudo-labeling to leverage unlabeled point clouds. However, producing reliable pseudo-labels in a diverse 3D space still remains challenging. In this work, we propose Diffusion-SS3D, a new perspective of enhancing the quality of pseudo-labels via the diffusion model for semi-supervised 3D object detection. Specifically, we include noises to produce corrupted 3D object size and class label distributions, and then utilize the diffusion model as a denoising process to obtain bounding box outputs. Moreover, we integrate the diffusion model into the teacher-student framework, so that the denoised bounding boxes can be used to improve pseudo-label generation, as well as the entire semi-supervised learning process. We conduct experiments on the ScanNet and SUN RGB-D benchmark datasets to demonstrate that our approach achieves state-of-the-art performance against existing methods. We also present extensive analysis to understand how our diffusion model design affects performance in semi-supervised learning.
We present a novel, power- & hardware-efficient, multiuser, multibeam RIS (Reflective Intelligent Surface) architecture for multiuser MIMO, especially for very high frequency bands (e.g., high mmWave and sub-THz), where channels are typically sparse in the beamspace and LOS is the dominant component. The key module is formed by an active multiantenna feeder (AMAF) with a small number of active antennas, placed in the near field of a RIS with a much larger number of passive controllable reflecting elements. We propose a pragmatic approach to obtain a steerable beam with high gain and very low sidelobes. Then K independently controlled beams can be achieved by closely stacking K such AMAF-RIS modules. Our analysis includes the mutual interference between the modules and the fact that, due to the delay difference of propagation through the AMAF-RIS structure, the resulting channel matrix is frequency selective even for pure LOS propagation. We consider a 3D geometry and show that "beam focusing" is in fact possible (and much more effective in terms of coverage) also in the far-field, by creating spotbeams with limited footprint both in angle and in range. Our results show that: 1) simple RF beamforming (BF) without computationally expensive baseband multiuser precoding is sufficient to practically eliminate multiuser interference when the users are chosen with sufficient angular/range separation, thanks to the extremely low sidelobe beams; 2) the impact of beam pointing errors with standard deviation as large as 2.5 deg and RIS quantized phase-shifters with quantization bits > 2 is essentially negligible; 3) The proposed architecture is more power efficient & much simpler from a hardware implementation viewpoint than standard active arrays with the same BF performance. We show also that the array gain of the proposed AMAF-RIS structure is linear with the RIS aperture.
The Open Radio Access Network (O-RAN) architecture empowers intelligent and automated optimization of the RAN through applications deployed on the RAN Intelligent Controller (RIC) platform, enabling capabilities beyond what is achievable with traditional RAN solutions. Within this paradigm, Traffic Steering (TS) emerges as a pivotal RIC application that focuses on optimizing cell-level mobility settings in near-real-time, aiming to significantly improve network spectral efficiency. In this paper, we design a novel TS algorithm based on a Cascade Reinforcement Learning (CaRL) framework. We propose state space factorization and policy decomposition to reduce the need for large models and well-labeled datasets. For each sub-state space, an RL sub-policy will be trained to learn an optimized mapping onto the action space. To apply CaRL on new network regions, we propose a knowledge transfer approach to initialize a new sub-policy based on knowledge learned by the trained policies. To evaluate CaRL, we build a data-driven and scalable RIC digital twin (DT) that is modeled using important real-world data, including network configuration, user geo-distribution, and traffic demand, among others, from a tier-1 mobile operator in the US. We evaluate CaRL on two DT scenarios representing two network clusters in two different cities and compare its performance with the business-as-usual (BAU) policy and other competing optimization approaches using heuristic and Q-table algorithms. Benchmarking results show that CaRL performs the best and improves the average cluster-aggregated downlink throughput over the BAU policy by 24% and 18% in these two scenarios, respectively.
Large vision-language models (LVLMs) have demonstrated their incredible capability in image understanding and response generation. However, this rich visual interaction also makes LVLMs vulnerable to adversarial examples. In this paper, we formulate a novel and practical gray-box attack scenario that the adversary can only access the visual encoder of the victim LVLM, without the knowledge of its prompts (which are often proprietary for service providers and not publicly available) and its underlying large language model (LLM). This practical setting poses challenges to the cross-prompt and cross-model transferability of targeted adversarial attack, which aims to confuse the LVLM to output a response that is semantically similar to the attacker's chosen target text. To this end, we propose an instruction-tuned targeted attack (dubbed InstructTA) to deliver the targeted adversarial attack on LVLMs with high transferability. Initially, we utilize a public text-to-image generative model to "reverse" the target response into a target image, and employ GPT-4 to infer a reasonable instruction $\boldsymbol{p}^\prime$ from the target response. We then form a local surrogate model (sharing the same visual encoder with the victim LVLM) to extract instruction-aware features of an adversarial image example and the target image, and minimize the distance between these two features to optimize the adversarial example. To further improve the transferability, we augment the instruction $\boldsymbol{p}^\prime$ with instructions paraphrased from an LLM. Extensive experiments demonstrate the superiority of our proposed method in targeted attack performance and transferability.
Intelligent driving systems aim to achieve a zero-collision mobility experience, requiring interdisciplinary efforts to enhance safety performance. This work focuses on risk identification, the process of identifying and analyzing risks stemming from dynamic traffic participants and unexpected events. While significant advances have been made in the community, the current evaluation of different risk identification algorithms uses independent datasets, leading to difficulty in direct comparison and hindering collective progress toward safety performance enhancement. To address this limitation, we introduce \textbf{RiskBench}, a large-scale scenario-based benchmark for risk identification. We design a scenario taxonomy and augmentation pipeline to enable a systematic collection of ground truth risks under diverse scenarios. We assess the ability of ten algorithms to (1) detect and locate risks, (2) anticipate risks, and (3) facilitate decision-making. We conduct extensive experiments and summarize future research on risk identification. Our aim is to encourage collaborative endeavors in achieving a society with zero collisions. We have made our dataset and benchmark toolkit publicly on the project page: //hcis-lab.github.io/RiskBench/
ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, we instead adopt the following domain adaptation techniques: custom tokenizers, domain-adaptive continued pretraining, supervised fine-tuning (SFT) with domain-specific instructions, and domain-adapted retrieval models. We evaluate these methods on three selected LLM applications for chip design: an engineering assistant chatbot, EDA script generation, and bug summarization and analysis. Our results show that these domain adaptation techniques enable significant LLM performance improvements over general-purpose base models across the three evaluated applications, enabling up to 5x model size reduction with similar or better performance on a range of design tasks. Our findings also indicate that there's still room for improvement between our current results and ideal outcomes. We believe that further investigation of domain-adapted LLM approaches will help close this gap in the future.
The surge in real-time data collection across various industries has underscored the need for advanced anomaly detection in both univariate and multivariate time series data. Traditional methods, while comprehensive, often struggle to capture the complex interdependencies in such data. This paper introduces TransNAS-TSAD, a novel framework that synergizes transformer architecture with neural architecture search (NAS), enhanced through NSGA-II algorithm optimization. This innovative approach effectively tackles the complexities of both univariate and multivariate time series, balancing computational efficiency with detection accuracy. Our evaluation reveals that TransNAS-TSAD surpasses conventional anomaly detection models, demonstrating marked improvements in diverse data scenarios. We also propose the Efficiency-Accuracy-Complexity Score (EACS) as a new metric for assessing model performance, emphasizing the crucial balance between accuracy and computational resources. TransNAS-TSAD sets a new benchmark in time series anomaly detection, offering a versatile, efficient solution for complex real-world applications. This research paves the way for future developments in the field, highlighting its potential in a wide range of industry applications.
Video compression systems must support increasing bandwidth and data throughput at low cost and power, and can be limited by entropy coding bottlenecks. Efficiency can be greatly improved by parallelizing coding, which can be done at much larger scales with new neural-based codecs, but with some compression loss related to data organization. We analyze the bit rate overhead needed to support multiple bitstreams for concurrent decoding, and for its minimization propose a method for compressing parallel-decoding entry points, using bidirectional bitstream packing, and a new form of jointly optimizing arithmetic coding termination. It is shown that those techniques significantly lower the overhead, making it easier to reduce it to a small fraction of the average bitstream size, like, for example, less than 1% and 0.1% when the average number of bitstream bytes is respectively larger than 95 and 1,200 bytes.
Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision, e.g., GPT-3 and Swin Transformer. Although originally designed for prediction problems, it is natural to inquire about their suitability for sequential decision-making and reinforcement learning problems, which are typically beset by long-standing issues involving sample efficiency, credit assignment, and partial observability. In recent years, sequence models, especially the Transformer, have attracted increasing interest in the RL communities, spawning numerous approaches with notable effectiveness and generalizability. This survey presents a comprehensive overview of recent works aimed at solving sequential decision-making tasks with sequence models such as the Transformer, by discussing the connection between sequential decision-making and sequence modeling, and categorizing them based on the way they utilize the Transformer. Moreover, this paper puts forth various potential avenues for future research intending to improve the effectiveness of large sequence models for sequential decision-making, encompassing theoretical foundations, network architectures, algorithms, and efficient training systems. As this article has been accepted by the Frontiers of Computer Science, here is an early version, and the most up-to-date version can be found at //journal.hep.com.cn/fcs/EN/10.1007/s11704-023-2689-5
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