Speculative decoding improves LLM inference by generating and verifying multiple tokens in parallel, but existing systems suffer from suboptimal performance due to a mismatch between dynamic speculation and static runtime assumptions. We present Yggdrasil, a co-designed system that enables latency-optimal speculative decoding through context-aware tree drafting and compiler-friendly execution. Yggdrasil introduces an equal-growth tree structure for static graph compatibility, a latency-aware optimization objective for draft selection, and stage-based scheduling to reduce overhead. Yggdrasil supports unmodified LLMs and achieves up to $3.98\times$ speedup over state-of-the-art baselines across multiple hardware setups.

Zero knowledge Neural Networks draw increasing attention for guaranteeing computation integrity and privacy of neural networks (NNs) based on zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK) security scheme. However, the performance of zkSNARK NNs is far from optimal due to the million-scale circuit computation with heavy scalar-level dependency. In this paper, we propose a type-based optimizing framework for efficient zero-knowledge NN inference, namely ZENO (ZEro knowledge Neural network Optimizer). We first introduce ZENO language construct to maintain high-level semantics and the type information (e.g., privacy and tensor) for allowing more aggressive optimizations. We then propose privacy-type driven and tensor-type driven optimizations to further optimize the generated zkSNARK circuit. Finally, we design a set of NN-centric system optimizations to further accelerate zkSNARK NNs. Experimental results show that ZENO achieves up to 8.5× end-to-end speedup than state-of-the-art zkSNARK NNs. We reduce proof time for VGG16 from 6 minutes to 48 seconds, which makes zkSNARK NNs practical.

Ensuring high-quality recommendations for newly onboarded users requires the continuous retraining of Deep Learning Recommendation Models (DLRMs) with freshly generated data. To serve the online DLRM retraining, existing solutions use hundreds of CPU computing nodes designated for input preprocessing, causing significant power consumption that surpasses even the power usage of GPU trainers.

With the rapid growth of classification scale in deep learning systems, the final classification layer becomes extreme classification with a memory footprint exceeding the main memory capacity of the CPU or GPU. The emerging in-storage-computing technique offers an opportunity on account of the fact that SSD has enough storage capacity for the parameters of extreme classification. However, the limited performance of naive in-storage-computing schemes is insufficient to support the heavy workload of extreme classification.

Deep learning Recommendation Models (DLRMs) plays an important role in various application domains. However, existing DLRM training systems require a large number of GPUs due to the memory-intensive embedding tables. To this end, we propose EL-Rec, an efficient computing framework harnessing the Tensor-train (TT) technique to democratize the training of large-scale DLRMs with limited GPU resources. Specifically, EL-Rec optimizes TT decomposition based on key computation primitives of embedding tables and implements a high-performance compressed embedding table which is a drop-in replacement of Pytorch API. EL-Rec introduces an index reordering technique to harvest the performance gains from both local and global information of training inputs. EL-Rec also highlights a pipeline training paradigm to eliminate the communication overhead between the host memory and the training worker. Comprehensive experiments demonstrate that EL-Rec can handle the largest publicly available DLRM dataset with a single GPU and achieves 3× speedup over the state-of-the-art DLRM frameworks.