True random number generators (TRNG) sample random physical processes to create large amounts of random numbers for various use cases, including security-critical cryptographic primitives, scientific simulations, machine learning applications, and even recreational entertainment. Unfortunately, not every computing system is equipped with dedicated TRNG hardware, limiting the application space and security guarantees for such systems. To open the application space and enable security guarantees for the overwhelming majority of computing systems that do not necessarily have dedicated TRNG hardware (e.g., processing-in-memory systems), we develop QUAC-TRNG, a new high-throughput TRNG that can be fully implemented in commodity DRAM chips, which are key components in most modern systems. QUAC-TRNG exploits the new observation that a carefully-engineered sequence of DRAM commands activates four consecutive DRAM rows in rapid succession. This QUadruple ACtivation (QUAC) causes the bitline sense amplifiers to non-deterministically converge to random values when we activate four rows that store conflicting data because the net deviation in bitline voltage fails to meet reliable sensing margins. We experimentally demonstrate that QUAC reliably generates random values across 136 commodity DDR4 DRAM chips from one major DRAM manufacturer. We describe how to develop an effective TRNG (QUAC-TRNG) based on QUAC. We evaluate the quality of our TRNG using the commonly-used NIST statistical test suite for randomness and find that QUAC-TRNG successfully passes each test. Our experimental evaluations show that QUAC-TRNG reliably generates true random numbers with a throughput of 3.44 Gb/s (per DRAM channel), outperforming the state-of-the-art DRAM-based TRNG by 15.08X and 1.41X for basic and throughput-optimized versions, respectively. We show that QUAC-TRNG utilizes DRAM bandwidth better than the state-of-the-art, achieving up to 2.03X the throughput of a throughput-optimized baseline when scaling bus frequencies to 12 GT/s.

QUAC-TRNG: high-throughput true random number generation using quadruple row activation in commodity DRAM chips

Many modern workloads, such as neural networks, databases, and graph processing, are fundamentally memory-bound. For such workloads, the data movement between main memory and CPU cores imposes a significant overhead in terms of both latency and energy. A major reason is that this communication happens through a narrow bus with high latency and limited bandwidth, and the low data reuse in memory-bound workloads is insufficient to amortize the cost of main memory access. Fundamentally addressing this data movement bottleneck requires a paradigm where the memory system assumes an active role in computing by integrating processing capabilities. This paradigm is known as processing-in-memory (PIM). Recent research explores different forms of PIM architectures, motivated by the emergence of new 3D-stacked memory technologies that integrate memory with a logic layer where processing elements can be easily placed. Past works evaluate these architectures in simulation or, at best, with simplified hardware prototypes. In contrast, the UPMEM company has designed and manufactured the first publicly-available real-world PIM architecture. The UPMEM PIM architecture combines traditional DRAM memory arrays with general-purpose in-order cores, called DRAM Processing Units (DPUs), integrated in the same chip. This paper provides the first comprehensive analysis of the first publicly-available real-world PIM architecture. We make two key contributions. First, we conduct an experimental characterization of the UPMEM-based PIM system using microbenchmarks to assess various architecture limits such as compute throughput and memory bandwidth, yielding new insights. Second, we present PrIM (Processing-In-Memory benchmarks), a benchmark suite of 16 workloads from different application domains (e.g., dense/sparse linear algebra, databases, data analytics, graph processing, neural networks, bioinformatics, image processing), which we identify as memory-bound. We evaluate the performance and scaling characteristics of PrIM benchmarks on the UPMEM PIM architecture, and compare their performance and energy consumption to their modern CPU and GPU counterparts. Our extensive evaluation conducted on two real UPMEM-based PIM systems with 640 and 2,556 DPUs provides new insights about suitability of different workloads to the PIM system, programming recommendations for software designers, and suggestions and hints for hardware and architecture designers of future PIM systems.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09771457.pdf

Benchmarking a New Paradigm: Experimental Analysis and Characterization of a Real Processing-in-Memory System

Personalized recommendation systems are gaining significant traction due to their industrial importance. An important building block of recommendation systems consists of the embedding layers, which exhibit a highly memory-intensive characteristic. A fundamental primitive of embedding layers is the embedding vector gathers followed by vector reductions, exhibiting low arithmetic intensity and becoming bottlenecked by the memory throughput. To tackle such a challenge, recent proposals employ a near-data processing (NDP) solution at the DRAM rank-level, achieving impressive performance speedups. We observe that prior rank-level-parallelism-based NDP solutions leave significant performance potential on the table as they do not fully reap the abundant transfer throughput inherent in DRAM datapaths. We propose TRiM, an NDP architecture for accelerating recommendation systems. Based on the observation that the DRAM datapath has a hierarchical tree structure, TRiM augments the DRAM datapath with “in-DRAM” reduction units at the DDR4/5 rank/bank-group/bank level. We modify the interface of DRAM to provide commands effectively to multiple reduction units running in parallel. We also propose a host-side architecture with hot embedding-vector replication to alleviate the load imbalance that arises across the reduction units. An optimal TRiM design based on DDR5 achieves up to a 7.7 × and 3.9 × speedup and reduces by 55% and 50% the energy consumption of the embedding vector gather and reduction over the baseline and the state-of-the-art NDP architecture with minimal area overhead equivalent to 2.66% of DRAM chips.

https://dl.acm.org/doi/pdf/10.1145/3466752.3480080

TRiM: Enhancing Processor-Memory Interfaces with Scalable Tensor Reduction in Memory

Many modern workloads, such as neural networks, databases, and graph processing, are fundamentally memory-bound. For such workloads, the data movement between main memory and CPU cores imposes a significant overhead in terms of both latency and energy. A major reason is that this communication happens through a narrow bus with high latency and limited bandwidth, and the low data reuse in memory-bound workloads is insufficient to amortize the cost of main memory access. Fundamentally addressing this data movement bottleneck requires a paradigm where the memory system assumes an active role in computing by integrating processing capabilities. This paradigm is known as processing-in-memory (PIM). 
Recent research explores different forms of PIM architectures, motivated by the emergence of new 3D-stacked memory technologies that integrate memory with a logic layer where processing elements can be easily placed. Past works evaluate these architectures in simulation or, at best, with simplified hardware prototypes. In contrast, the UPMEM company has designed and manufactured the first publicly-available real-world PIM architecture. 
This paper provides the first comprehensive analysis of the first publicly-available real-world PIM architecture. We make two key contributions. First, we conduct an experimental characterization of the UPMEM-based PIM system using microbenchmarks to assess various architecture limits such as compute throughput and memory bandwidth, yielding new insights. Second, we present PrIM, a benchmark suite of 16 workloads from different application domains (e.g., linear algebra, databases, graph processing, neural networks, bioinformatics).

Benchmarking a New Paradigm: An Experimental Analysis of a Real Processing-in-Memory Architecture.

With advances in deep-neural-network applications the increasingly large data movement through memory channels is becoming inevitable: specifically, RNN and MLP applications are memory bound and the memory is the performance bottleneck [1]. DRAM featuring processing in memory (PIM) significantly reduces data movement [1]–[4], and the system performance is enhanced by the large internal parallel bank bandwidth. Among DRAM-based PIM proposals, [3] is near commercialization, but the required HBM technology may prevent it from being applied to other applications due to its high cost [5]. In this situation, an accelerator-in-memory (AiM) based on GDDR6 may be applicable: it has a relatively low-cost, is compatible with GDDR6 interface, and is designed to accelerate deep-learning (DL) applications. AiM offers a peak throughput of 1 TFLOPS with processing units (PUs) with a speed of 1 GHz utilizing the characteristics of GDDR6 with a speed of 16Gb/s. It can also support many applications as it has various activation functions. This paper first looks at the AiM architecture and the supported command set for DL operations. Next, the DL operations in the PU and supported activation functions are described. Finally, we present evaluation results of DL behavior of AiM at the package and the system level. 

A 1ynm 1.25V 8Gb, 16Gb/s/pin GDDR6-based Accelerator-in-Memory supporting 1TFLOPS MAC Operation and Various Activation Functions for Deep-Learning Applications

In recent years, artificial intelligence (AI) technology has proliferated rapidly and widely into application areas such as speech recognition, health care, and autonomous driving. To increase the capabilities of AI more powerful systems are needed to process a larger amount of data. This requirement has made domain-specific accelerators, such as GPUs and TPUs, popular; as they can provide orders of magnitude higher performance than state-of-the-art CPUs. However, these accelerators can only operate at their peak performance when they get the necessary data from memory as quickly as it is processed: requiring off-chip memory with a high bandwidth and a large capacity [1]. HBM has thus far met the bandwidth and capacity requirement [2] –[6], but recent AI technologies such as recurrent neural networks require an even higher bandwidth than HBM [7]–[8]. While a further increase in off-chip bandwidth can be accomplished by various techniques, it is often limited by power constraints at the chip or system level [9]. Hence, it is essential to decrease demand for off-chip bandwidth with unconventional architectures: such as processing-in-memory. In this paper, we present function-In-memory DRAM (FIMDRAM) that integrates a 16-wide single-instruction multiple-data engine within the memory banks and that exploits bank-level parallelism to provide $4 \times $ higher processing bandwidth than an off-chip memory solution. Second, we show techniques that do not require any modification to conventional memory controllers and their command protocols, which make FIMDRAM more practical for quick industry adoption. Finally, we conclude this paper with circuit- and system-level evaluations of our fabricated FIMDRAM.

25.4 A 20nm 6GB Function-In-Memory DRAM, Based on HBM2 with a 1.2TFLOPS Programmable Computing Unit Using Bank-Level Parallelism, for Machine Learning Applications

Jin Kim

Papers

25.4 A 20nm 6GB Function-In-Memory DRAM, Based on HBM2 with a 1.2TFLOPS Programmable Computing Unit Using Bank-Level Parallelism, for Machine Learning Applications