Large language models (LLMs) have demonstrated exceptional capabilities in generating text, images, and video content. However, as context length grows, the computational cost of attention increases quadratically with the number of tokens, presenting significant efficiency challenges. This paper presents an analysis of various Key-Value (KV) cache compression strategies, offering a comprehensive taxonomy that categorizes these methods by their underlying principles and implementation techniques. Furthermore, we evaluate their impact on performance and inference latency, providing critical insights into their effectiveness. Our findings highlight the trade-offs involved in KV cache compression and its influence on handling long-context scenarios, paving the way for more efficient LLM implementations.

Mixture-of-Experts (MoE) Transformer, the backbone architecture The profound impact of the Transformer architecture in the domain of multiple phenomenal language models, leverages sparsity of machine learning is undeniable, for the fields including by activating only a fraction of model parameters for each input natural language processing [3, 14, 18, 45, 48, 61] and computer token. The sparse structure, while allowing constant time costs, vision [17, 39, 64], to name a few. To further improve the capabilities results in space inefficiency: we still need to load all the model of pre-trained large language models (LLMs), one general parameters during inference. We introduce ResMoE, an innovative strategy is to scale up their parameters. Mixture-of-Experts (MoE) MoE approximation framework that utilizes Wasserstein barycenter [52] extends the traditional feedforward neural network (FFN) layer to extract a common expert (barycenter expert) and approximate by replacing a single multilayer perceptron (MLP) with multiple the residuals between this barycenter expert and the original ones. MLPs, referred to as "experts". While enhancing the performance, ResMoE enhances the space efficiency for inference of large-scale sparse MoE keeps computing costs (FLOPs) comparable to the original MoE Transformers in a one-shot and data-agnostic manner without dense model, as only a few selected experts will be activated retraining while maintaining minimal accuracy loss, thereby each time. The framework of an MoE layer is demonstrated in paving the way for broader accessibility to large language models.

Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that combine a per-block scaling factor with narrow floating-point and integer types for individual elements. MX formats balance the competing needs of hardware efficiency, model accuracy, and user friction. Empirical results on over two dozen benchmarks demonstrate practicality of MX data formats as a drop-in replacement for baseline FP32 for AI inference and training with low user friction. We also show the first instance of training generative language models at sub-8-bit weights, activations, and gradients with minimal accuracy loss and no modifications to the training recipe.

Training large and highly accurate deep learning (DL) models is computationally costly. This cost is in great part due to the excessive number of trained parameters, which are well-known to be redundant and compressible for the execution phase. This paper proposes a novel transformation which changes the topology of the DL architecture such that it reaches an optimal cross-layer connectivity. This transformation leverages our important observation that for a set level of accuracy, convergence is fastest when network topology reaches the boundary of a Small-World Network. Small-world graphs are known to possess a specific connectivity structure that enables enhanced signal propagation among nodes. Our small-world models, called SWNets, provide several intriguing benefits: they facilitate data (gradient) flow within the network, enable feature-map reuse by adding long-range connections and accommodate various network architectures/datasets. Compared to densely connected networks (e.g., DenseNets), SWNets require a substantially fewer number of training parameters while maintaining a similar level of classification accuracy. We evaluate our networks on various DL model architectures and image classification datasets, namely, CIFAR10, CIFAR100, and ILSVRC (ImageNet). Our experiments demonstrate an average of ~2.1x improvement in convergence speed to the desired accuracy

The success of deep learning models is heavily tied to the use of massive amount of labeled data and excessively long training time. With the emergence of intelligent edge applications that use these models, the critical challenge is to obtain the same inference capability on a resource-constrained device while providing adaptability to cope with the dynamic changes in the data. We propose AgileNet, a novel lightweight dictionary-based few-shot learning methodology which provides reduced complexity deep neural network for efficient execution at the edge while enabling low-cost updates to capture the dynamics of the new data. Evaluations of state-of-the-art few-shot learning benchmarks demonstrate the superior accuracy of AgileNet compared to prior arts. Additionally, AgileNet is the first few-shot learning approach that prevents model updates by eliminating the knowledge obtained from the primary training. This property is ensured through the dictionaries learned by our novel end-to-end structured decomposition, which also reduces the memory footprint and computation complexity to match the edge device constraints.

This paper proposes CuRTAIL, an end-to-end computing framework for characterizing and thwarting adversarial space in the context of Deep Learning (DL). The framework protects deep neural networks against adversarial samples, which are perturbed inputs carefully crafted by malicious entities to mislead the underlying DL model. The precursor for the proposed methodology is a set of new quantitative metrics to assess the vulnerability of various deep learning architectures to adversarial samples. CuRTAIL formalizes the goal of preventing adversarial samples as a minimization of the space unexplored by the pertinent DL model that is characterized in CuRTAIL vulnerability analysis step. To thwart the adversarial machine learning attack, CuRTAIL introduces the concept of Modular Robust Redundancy (MRR) as a viable solution to achieve the formalized minimization objective. The MRR methodology explicitly characterizes the geometry of the input data and the DL model parameters. It then learns a set of complementary but disjoint models which maximally cover the unexplored subspaces of the target DL model, thus reducing the risk of integrity attacks. We extensively evaluate CuRTAIL performance against the state-of-the-art attack models including fast-sign-gradient, Jacobian Saliency Map Attack, Deepfool, and Carlini&WagnerL2. Proof-of-concept implementations for analyzing various data collections including MNIST, CIFAR10, and ImageNet corroborate CuRTAIL effectiveness to detect adversarial samples in different settings. The computations in each MRR module can be performed independently. As such, CuRTAIL detection algorithm can be completely parallelized among multiple hardware settings to achieve maximum throughput. We further provide an accompanying API to facilitate the adoption of the proposed framework for various applications.