Statistical machine learning has widespread application in various domains. These methods include probabilistic algorithms, such as Markov Chain Monte-Carlo (MCMC), which rely on generating random numbers from probability distributions. These algorithms are computationally expensive on conventional processors, yet their statistical properties, namely interpretability and uncertainty quantification (UQ) compared to deep learning, make them an attractive alternative approach. Therefore, hardware specialization can be adopted to address the shortcomings of conventional processors in running these applications. In this paper, we propose a high-throughput accelerator for Markov Random Field (MRF) inference, a powerful model for representing a wide range of applications, using MCMC with Gibbs sampling. We propose a tiled architecture which takes advantage of near-memory computing, and memory optimizations tailored to the semantics of MRF. Additionally, we propose a novel hybrid on-chip/off-chip memory system and logging scheme to efficiently support UQ. This memory system design is not specific to MRF models and is applicable to applications using probabilistic algorithms. In addition, it dramatically reduces off-chip memory bandwidth requirements. We implemented an FPGA prototype of our proposed architecture using high-level synthesis tools and achieved 146MHz frequency for an accelerator with 32 function units on an Intel Arria 10 FPGA. Compared to prior work on FPGA, our accelerator achieves 26X speedup. Furthermore, our proposed memory system and logging scheme to support UQ reduces off-chip bandwidth by 71% for two applications. ASIC analysis in 15nm shows our design with 2048 function units running at 3GHz outperforms GPU implementations of motion estimation and stereo vision on Nvidia RTX2080Ti by 120X-210X, occupying only 7.7% of the area.
翻译:统计机学习在多个领域广泛应用。 这些方法包括概率算法, 比如 Markov 链链 Monte-Carlo (MCMC), 它依靠概率分布生成随机数字。 这些算法在常规处理器上计算成本昂贵, 然而它们的统计属性, 即可解释性和不确定性量化(UQ) 与深层学习相比, 使得它们成为一种有吸引力的替代方法。 因此, 硬件专业化可以用来解决常规处理器在运行这些应用程序中的缺陷。 在本文中, 我们提议为Markov Rand Field(MRF) 的推算提供一个高通性加速器。 一个代表广泛应用的强大模型, 使用 Gbbs 抽样取样的 MC 。 我们提出了一个具有近似模量计算功能的平面结构, 与MRFS的缩略图相匹配。 此外, 我们提出了一个新的在芯片/off- 存储器上配置一个高级直径直径直径直径直径直径直径直径直径直径直径直径直的直径直径直径直径直径直径直径直径直径直径直径直径直径直径直径直径直径直径直直的系统。