Workload traces are essential to understand complex computer systems' behavior and manage processing and memory resources.Since real-world traces are hard to obtain, synthetic trace generation is a promising alternative.This paper proposes a first-of-a-kind approach that relies on training a large language model (LLM) to generate synthetic workload traces, specifically microservice call graphs.To capture complex and arbitrary hierarchical structures and implicit constraints in such traces, we propose to train LLMs to generate recursively, making call graph generation a sequence of more manageable steps.To further enforce learning constraints on the traces and generate uncommon situations, we apply additional instruction tuning steps to align our model with the desired trace features.With this method, we train TraceLLM, an LLM for microservice trace generation, and demonstrate that it produces diverse, realistic traces under varied conditions, outperforming existing approaches in both accuracy and validity.The synthetically generated traces can effectively replace real data to optimize important microservice management tasks.Additionally, TraceLLM adapts to downstream trace-related tasks, such as predicting key trace features and infilling missing data.

A key problem when modeling signal integrity for passive filters and interconnects in IC packages is the need for multiple S-parameter measurements within a desired frequency band to obtain adequate resolution. These samples are often computationally expensive to obtain using electromagnetic (EM) field solvers. Therefore, a common approach is to select a small subset of the necessary samples and use an appropriate fitting mechanism to recreate a densely-sampled broadband representation. We present the first deep generative model-based approach to fit S-parameters from EM solvers using one-dimensional Deep Image Prior (DIP). DIP is a technique that optimizes the weights of a randomly-initialized convolutional neural network to fit a signal from noisy or under-determined measurements. We design a custom architecture and propose a novel regularization inspired by smoothing splines that penalizes discontinuous jumps. We experimentally compare DIP to publicly available and proprietary industrial implementations of Vector Fitting (VF), the industry-standard tool for fitting S-parameters. Relative to publicly available implementations of VF, our method shows superior performance on nearly all test examples using only 5 – 15% of the frequency samples. Our method is also competitive to proprietary VF tools and often outperforms them for challenging input instances.

Many natural signals lie in a union of subspaces, which we can exploit when compressing these signals to maintain a high level of fidelity while significantly reducing the storage size. Standard compression techniques for natural signals such as images follow a general pipeline which uses predetermined transformations to encode and decode data. Recent advances in deep learning-based techniques for compressing image data have shown results which compete with existing compression standards. Inspired by the success of these deep learning methods, we evaluate various classical and deep learning-based methods for encoding and decoding signals which follow a union-of-subspaces structure. On the classical side, we evaluate compressed sensing with a learned dictionary, whereas for deep learning-based techniques, we consider an autoencoder and a deep generative model-based variant of compressed sensing. Our results suggest that while classical compressed sensing-based methods work well, deep learning-based techniques perform better as the union-of-subspaces signal structure becomes more complex.

Techniques which leverage channel state information (CSI) at a transmitter to adapt wireless signals to changing propagation conditions have been shown to improve the reliability of modern multiple input multiple output (MIMO) communication systems. To reduce overhead, previous works have proposed to compress CSI matrices using a trained deep autoencoder (AE) at the receiver before feeding it back to the transmitter, and recent work has proposed to quantize and perform entropy coding on the compressed CSI to further reduce communication complexity. While these methods are effective, they either do not incorporate quantization and lossless coding into their end-to-end optimization, or do not achieve performance comparable to methods that do not use quantization and entropy coding. In this work, we propose a new AE-based feedback method which uses an entropy bottleneck layer to both quantize and losslessly code the compressed CSI. This bottleneck layer allows us to jointly optimize bit-rate and distortion to achieve a highly-compressed CSI representation which preserves important channel information. Our method achieves better reconstruction quality than existing autoencoder-based CSI feedback methods for a wide range of bit-rates on simulated data, in both indoor and outdoor wireless settings.

We study a new family of inverse problems for recovering representations of corrupted data. We assume access to a pre-trained representation learning network R(x) that operates on clean images, like CLIP. The problem is to recover the representation of an image R(x), if we are only given a corrupted version A(x), for some known forward operator A. We propose a supervised inversion method that uses a contrastive objective to obtain excellent representations for highly corrupted images. Using a linear probe on our robust representations, we achieve a higher accuracy than end-to-end supervised baselines when classifying images with various types of distortions, including blurring, additive noise, and random pixel masking. We evaluate on a subset of ImageNet and observe that our method is robust to varying levels of distortion. Our method outperforms end-to-end baselines even with a fraction of the labeled data in a wide range of forward operators.