DeepNVM++: Cross-Layer Modeling and Optimization Framework of Non-Volatile Memories for Deep Learning

Non-volatile memory (NVM) technologies such as spin-transfer torque magnetic random access memory (STT-MRAM) and spin-orbit torque magnetic random access memory (SOT-MRAM) have significant advantages compared to conventional SRAM due to their non-volatility, higher cell density, and scalability features. While previous work has investigated several architectural implications of NVM for generic applications, in this work we present DeepNVM++, a framework to characterize, model, and analyze NVM-based caches in GPU architectures for deep learning (DL) applications by combining technology-specific circuit-level models and the actual memory behavior of various DL workloads. We present both iso-capacity and iso-area performance and energy analysis for systems whose last-level caches rely on conventional SRAM and emerging STT-MRAM and SOT-MRAM technologies. In the iso-capacity case, STT-MRAM and SOT-MRAM provide up to 3.8x and 4.7x energy-delay product (EDP) reduction and 2.4x and 2.8x area reduction compared to conventional SRAM, respectively. Under iso-area assumptions, STT-MRAM and SOT-MRAM provide up to 2x and 2.3x EDP reduction and accommodate 2.3x and 3.3x cache capacity when compared to SRAM, respectively. We also perform a scalability analysis and show that STT-MRAM and SOT-MRAM achieve orders of magnitude EDP reduction when compared to SRAM for large cache capacities. Our comprehensive cross-layer framework is demonstrated on STT-/SOT-MRAM technologies and can be used for the characterization, modeling, and analysis of any NVM technology for last-level caches in GPUs for DL applications.Comment: 12 pages, 10 figure

System and Design Technology Co-optimization of SOT-MRAM for High-Performance AI Accelerator Memory System

SoCs are now designed with their own AI accelerator segment to accommodate the ever-increasing demand of Deep Learning (DL) applications. With powerful MAC engines for matrix multiplications, these accelerators show high computing performance. However, because of limited memory resources (i.e., bandwidth and capacity), they fail to achieve optimum system performance during large batch training and inference. In this work, we propose a memory system with high on-chip capacity and bandwidth to shift the gear of AI accelerators from memory-bound to achieving system-level peak performance. We develop the memory system with DTCO-enabled customized SOT-MRAM as large on-chip memory through STCO and detailed characterization of the DL workloads. %We evaluate our workload-aware memory system on the CV and NLP benchmarks and observe significant PPA improvement compared to an SRAM-based in both inference and training modes. Our workload-aware memory system achieves 8X energy and 9X latency improvement on Computer Vision (CV) benchmarks in training and 8X energy and 4.5X latency improvement on Natural Language Processing (NLP) benchmarks in training while consuming only around 50% of SRAM area at iso-capacity

Accelerating Time Series Analysis via Processing using Non-Volatile Memories

Time Series Analysis (TSA) is a critical workload for consumer-facing devices. Accelerating TSA is vital for many domains as it enables the extraction of valuable information and predict future events. The state-of-the-art algorithm in TSA is the subsequence Dynamic Time Warping (sDTW) algorithm. However, sDTW's computation complexity increases quadratically with the time series' length, resulting in two performance implications. First, the amount of data parallelism available is significantly higher than the small number of processing units enabled by commodity systems (e.g., CPUs). Second, sDTW is bottlenecked by memory because it 1) has low arithmetic intensity and 2) incurs a large memory footprint. To tackle these two challenges, we leverage Processing-using-Memory (PuM) by performing in-situ computation where data resides, using the memory cells. PuM provides a promising solution to alleviate data movement bottlenecks and exposes immense parallelism. In this work, we present MATSA, the first MRAM-based Accelerator for Time Series Analysis. The key idea is to exploit magneto-resistive memory crossbars to enable energy-efficient and fast time series computation in memory. MATSA provides the following key benefits: 1) it leverages high levels of parallelism in the memory substrate by exploiting column-wise arithmetic operations, and 2) it significantly reduces the data movement costs performing computation using the memory cells. We evaluate three versions of MATSA to match the requirements of different environments (e.g., embedded, desktop, or HPC computing) based on MRAM technology trends. We perform a design space exploration and demonstrate that our HPC version of MATSA can improve performance by 7.35x/6.15x/6.31x and energy efficiency by 11.29x/4.21x/2.65x over server CPU, GPU and PNM architectures, respectively