Spiking neural networks (SNNs) have emerged as a promising paradigm for effective event-driven computation. However, CMOS-based SNN designs are limited by power consumption and complexity, while nonvolatile memory (NVM)-based SNN designs often lack biological characteristics and require active capacitive circuits to emulate neuronal dynamics. In this paper, we propose a thermally interplayed spin-orbit torque magnetic tunnel junction (TI-MTJ) macro that integrates core SNN functionalities. Our neuron array autonomously achieves leaky integrate-and-fire (LIF) model within the TI-MTJ device, thus improving power efficiency and simplifying circuit structure. Additionally, the proposed synaptic array provides adaptive in-situ responses based on a simplified spike-timing-dependent plasticity (STDP) rule. To enhance biological plausibility, our macro incorporates real-time spike monitoring and inhibition mechanisms. A comprehensive device-circuit-algorithm co-optimization framework validates the high performance of the TI-MTJ macro, achieving a synaptic energy consumption of 6.07fJ per spike, an inference accuracy of 97.76% on the MNIST dataset, and an energy efficiency of 22.8TOPS/W.

Downlink precoding in massive multiple input multiple output (MIMO) systems involves high-dimensional sparse matrix calculations, which poses challenges to existing architectures. Computing-in-memory (CIM) has significant advantages in handling large-scale parallel operations, but sparse computing for wireless communication remains underexplored. In this paper, we propose a novel CIM accelerator based on magnetic random access memory (MRAM) leveraging adaptive multi-sparse mode technology for optimized sparse matrix multiplication in MIMO communication systems. This architecture represents the first application of CIM technology for processing sparse matrices in MIMO precoding tasks, minimizing storage requirements and enhancing parallel processing speed. Experimental results demonstrate that, for a 32×256×8 MIMO downlink precoding task with 90% sparsity, the symbol error rate is reduced to 0.1% at a signal-to-noise ratio of 20dB, achieving 8.35× reduction in storage overhead, 39.4× power saving and 9.85× speedup. These results position our accelerator as a promising candidate for processing sparse data in 5G massive MIMO systems.

Recent advancements in source code summarization have leveraged transformer-based pre-trained models, including Large Language Models of Code (LLMCs), to automate and improve the generation of code summaries. However, existing methods often focus on generating a single high-quality summary for a given source code, neglecting scenarios where the generated summary might be inadequate and alternative options are needed. In this paper, we introduce Variational Prefix Tuning (VPT), a novel approach that enhances pre-trained models’ ability to generate diverse yet accurate sets of summaries, allowing the user to choose the most suitable one for the given source code. Our method integrates a Conditional Variational Autoencoder (CVAE) framework as a modular component into pre-trained models, enabling us to model the distribution of observed target summaries and sample continuous embeddings to be used as prefixes to steer the generation of diverse outputs during decoding. Importantly, we construct our method in a parameter-efficient manner, eliminating the need for expensive model retraining, especially when using LLMCs. Furthermore, we employ a bi-criteria reranking method to select a subset of generated summaries, optimizing both the diversity and the accuracy of the options presented to users. We present extensive experimental evaluations using widely used datasets and current state-of-the-art pre-trained code summarization models to demonstrate the effectiveness of our approach and its adaptability across models.

In recent years, at ByteDance, we have started seeing more and more business scenarios that require performing real-time data serving besides complex Ad Hoc analysis over large amounts of freshly imported data. The serving workload requires performing complex queries over massive newly added data items with minimal delay. These systems are often used in mission-critical scenarios, whereas traditional OLAP systems cannot handle such use cases. To work around the problem, ByteDance products often have to use multiple systems together in production, forcing the same data to be ETLed into multiple systems, causing data consistency problems, wasting resources, and increasing learning and maintenance costs. To solve the above problem, we built a single Hybrid Serving and Analytical Processing (HSAP) system to handle both workload types. HSAP is still in its early stage, and very few systems are yet on the market. This paper demonstrates how to build Krypton, a competitive cloud-native HSAP system that provides both excellent elasticity and query performance by utilizing many previously known query processing techniques, a hierarchical cache with persistent memory, and a native columnar storage format. Krypton can support high data freshness, high data ingestion rates, and strong data consistency. We also discuss lessons and best practices we learned in developing and operating Krypton in production.