FT-Transformer: Resilient and Reliable Transformer with End-to-End Fault Tolerant Attention

Transformer models leverage self-attention mechanisms to capture complex dependencies, demonstrating exceptional performance in various applications. However, the long-duration high-load computations required for model inference impose stringent reliability demands on the computing platform, as soft errors that occur during execution can significantly degrade model performance. Existing fault tolerance methods protect each operation separately using decoupled kernels, incurring substantial computational and memory overhead. In this paper, we propose a novel error-resilient framework for Transformer models, integrating end-to-end fault tolerant attention (EFTA) to improve inference reliability against soft errors. Our approach enables error detection and correction within a fully fused attention kernel, reducing redundant data access and thereby mitigating memory faults. To further enhance error coverage and reduce overhead, we design a hybrid fault tolerance scheme tailored for the EFTA, introducing for the first time: 1) architecture-aware algorithm-based fault tolerance (ABFT) using tensor checksum, which minimizes inter-thread communication overhead on tensor cores during error detection; 2) selective neuron value restriction, which selectively applies adaptive fault tolerance constraints to neuron values, balancing error coverage and overhead; 3) unified verification, reusing checksums to streamline multiple computation steps into a single verification process. Experimental results show that EFTA achieves up to 7.56x speedup over traditional methods with an average fault tolerance overhead of 13.9%.

Mind the Memory Gap: Unveiling GPU Bottlenecks in Large-Batch LLM Inference

Large language models have been widely adopted across different tasks, but their auto-regressive generation nature often leads to inefficient resource utilization during inference. While batching is commonly used to increase throughput, performance gains plateau beyond a certain batch size, especially with smaller models, a phenomenon that existing literature typically explains as a shift to the compute-bound regime. In this paper, through an in-depth GPU-level analysis, we reveal that large-batch inference remains memory-bound, with most GPU compute capabilities underutilized due to DRAM bandwidth saturation as the primary bottleneck. To address this, we propose a Batching Configuration Advisor (BCA) that optimizes memory allocation, reducing GPU memory requirements with minimal impact on throughput. The freed memory and underutilized GPU compute capabilities can then be leveraged by concurrent workloads. Specifically, we use model replication to improve serving throughput and GPU utilization. Our findings challenge conventional assumptions about LLM inference, offering new insights and practical strategies for improving resource utilization, particularly for smaller language models.

KARST: Multi-Kernel Kronecker Adaptation with Re-Scaling Transmission for Visual Classification

Fine-tuning pre-trained vision models for specific tasks is a common practice in computer vision. However, this process becomes more expensive as models grow larger. Recently, parameter-efficient fine-tuning (PEFT) methods have emerged as a popular solution to improve training efficiency and reduce storage needs by tuning additional low-rank modules within pre-trained backbones. Despite their advantages, they struggle with limited representation capabilities and misalignment with pre-trained intermediate features. To address these issues, we introduce an innovative Multi-Kernel Kronecker Adaptation with Re-Scaling Transmission (KARST) for various recognition tasks. Specifically, its multi-kernel design extends Kronecker projections horizontally and separates adaptation matrices into multiple complementary spaces, reducing parameter dependency and creating more compact subspaces. Besides, it incorporates extra learnable re-scaling factors to better align with pre-trained feature distributions, allowing for more flexible and balanced feature aggregation. Extensive experiments validate that our KARST outperforms other PEFT counterparts with a negligible inference cost due to its re-parameterization characteristics. Code is publicly available at: https://github.com/Lucenova/KARST.

Towards Pareto Optimal Throughput in Small Language Model Serving

Large language models (LLMs) have revolutionized the state-of-the-art of many different natural language processing tasks. Although serving LLMs is computationally and memory demanding, the rise of Small Language Models (SLMs) offers new opportunities for resource-constrained users, who now are able to serve small models with cutting-edge performance. In this paper, we present a set of experiments designed to benchmark SLM inference at performance and energy levels. Our analysis provides a new perspective in serving, highlighting that the small memory footprint of SLMs allows for reaching the Pareto-optimal throughput within the resource capacity of a single accelerator. In this regard, we present an initial set of findings demonstrating how model replication can effectively improve resource utilization for serving SLMs.

The Case for Universal Basic Computing Power

The Universal Basic Computing Power (UBCP) initiative ensures global, free access to a set amount of computing power specifically for AI research and development (R&D). This initiative comprises three key elements. First, UBCP must be cost free, with its usage limited to AI R&D and minimal additional conditions. Second, UBCP should continually incorporate the state of the art AI advancements, including efficiently distilled, compressed, and deployed training data, foundational models, benchmarks, and governance tools. Lastly, it's essential for UBCP to be universally accessible, ensuring convenience for all users. We urge major stakeholders in AI development large platforms, open source contributors, and policymakers to prioritize the UBCP initiative.

Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generation

This study examines the effect of prompt engineering on the performance of Large Language Models (LLMs) in clinical note generation. We introduce an Automatic Prompt Optimization (APO) framework to refine initial prompts and compare the outputs of medical experts, non-medical experts, and APO-enhanced GPT3.5 and GPT4. Results highlight GPT4 APO's superior performance in standardizing prompt quality across clinical note sections. A human-in-the-loop approach shows that experts maintain content quality post-APO, with a preference for their own modifications, suggesting the value of expert customization. We recommend a two-phase optimization process, leveraging APO-GPT4 for consistency and expert input for personalization.

H3WB: Human3.6M 3D WholeBody Dataset and Benchmark

3D human whole-body pose estimation aims to localize precise 3D keypoints on the entire human body, including the face, hands, body, and feet. Due to the lack of a large-scale fully annotated 3D whole-body dataset, a common approach has been to train several deep networks separately on datasets dedicated to specific body parts, and combine them during inference. This approach suffers from complex training and inference pipelines because of the different biases in each dataset used. It also lacks a common benchmark which makes it difficult to compare different methods. To address these issues, we introduce Human3.6M 3D WholeBody (H3WB) which provides whole-body annotations for the Human3.6M dataset using the COCO Wholebody layout. H3WB is a large scale dataset with 133 whole-body keypoint annotations on 100K images, made possible by our new multi-view pipeline. Along with H3WB, we propose 3 tasks: i) 3D whole-body pose lifting from 2D complete whole-body pose, ii) 3D whole-body pose lifting from 2D incomplete whole-body pose, iii) 3D whole-body pose estimation from a single RGB image. We also report several baselines from popular methods for these tasks. The dataset is publicly available at \url{https://github.com/wholebody3d/wholebody3d}.

Decanus to Legatus: Synthetic training for 2D-3D human pose lifting

3D human pose estimation is a challenging task because of the difficulty to acquire ground-truth data outside of controlled environments. A number of further issues have been hindering progress in building a universal and robust model for this task, including domain gaps between different datasets, unseen actions between train and test datasets, various hardware settings and high cost of annotation, etc. In this paper, we propose an algorithm to generate infinite 3D synthetic human poses (Legatus) from a 3D pose distribution based on 10 initial handcrafted 3D poses (Decanus) during the training of a 2D to 3D human pose lifter neural network. Our results show that we can achieve 3D pose estimation performance comparable to methods using real data from specialized datasets but in a zero-shot setup, showing the generalization potential of our framework.

Intelligent Traffic Light via Policy-based Deep Reinforcement Learning

Intelligent traffic lights in smart cities can optimally reduce traffic congestion. In this study, we employ reinforcement learning to train the control agent of a traffic light on a simulator of urban mobility. As a difference from existing works, a policy-based deep reinforcement learning method, Proximal Policy Optimization (PPO), is utilized other than value-based methods such as Deep Q Network (DQN) and Double DQN (DDQN). At first, the obtained optimal policy from PPO is compared to those from DQN and DDQN. It is found that the policy from PPO performs better than the others. Next, instead of the fixed-interval traffic light phases, we adopt the light phases with variable time intervals, which result in a better policy to pass the traffic flow. Then, the effects of environment and action disturbances are studied to demonstrate the learning-based controller is robust. At last, we consider unbalanced traffic flows and find that an intelligent traffic light can perform moderately well for the unbalanced traffic scenarios, although it learns the optimal policy from the balanced traffic scenarios only.

Transferring Inter-Class Correlation

The Teacher-Student (T-S) framework is widely utilized in the classification tasks, through which the performance of one neural network (the student) can be improved by transferring knowledge from another trained neural network (the teacher). Since the transferring knowledge is related to the network capacities and structures between the teacher and the student, how to define efficient knowledge remains an open question. To address this issue, we design a novel transferring knowledge, the Self-Attention based Inter-Class Correlation (ICC) map in the output layer, and propose our T-S framework, Inter-Class Correlation Transfer (ICCT).