Search

Genome analysis has revolutionized fields such as personalized medicine and forensics. Modern sequencing machines generate vast amounts of fragmented strings of genome data called reads. The alignment of these reads into a complete DNA sequence of an organism (the read mapping process) requires extensive data transfer between processing units and memory, leading to execution bottlenecks. Prior studies have primarily focused on accelerating specific stages of the read-mapping task. Conversely, this paper introduces a holistic framework called DART-PIM that accelerates the entire read-mapping process. DART-PIM facilitates digital processing-in-memory (PIM) for an end-to-end acceleration of the entire read-mapping process, from indexing using a unique data organization schema to filtering and read alignment with an optimized Wagner Fischer algorithm. A comprehensive performance evaluation with real genomic data shows that DART-PIM achieves a 5.7x and 257x improvement in throughput and a 92x and 27x energy efficiency enhancement compared to state-of-the-art GPU and PIM implementations, respectively.

Volatile memristors have recently gained popularity as promising devices for neuromorphic circuits, capable of mimicking the leaky function of neurons and offering advantages over capacitor-based circuits in terms of power dissipation and area. Additionally, volatile memristors are useful as selector devices and for hardware security circuits such as physical unclonable functions. To facilitate the design and simulation of circuits, a compact behavioral model is essential. This paper proposes V-VTEAM, a compact, simple, general, and flexible behavioral model for volatile memristors, inspired by the VTEAM nonvolatile memristor model and developed in MATLAB. The validity of the model is demonstrated by fitting it to an ion drift/diffusion-based Ag/SiOx/C/W volatile memristor, achieving a relative root mean error square of 4.5%., Comment: 4 pages, 4 figures, 1 table, to be published in proceedings of 2024 International Flexible Electronics Technology Conference (IFETC 2024)

This paper experimentally demonstrates a near-crossbar memory logic technique called Pinatubo. Pinatubo, an acronym for Processing In Non-volatile memory ArchiTecture for bUlk Bitwise Operations, facilitates the concurrent activation of two or more rows, enabling bitwise operations such as OR, AND, XOR, and NOT on the activated rows. We implement Pinatubo using phase change memory (PCM) and compare our experimental results with the simulated data from the original Pinatubo study. Our findings highlight a significant four-orders of magnitude difference between resistance states, suggesting the robustness of the Pinatubo architecture with PCM technology.

Resistive Random Access Memory (RRAM) crossbar arrays are an attractive memory structure for emerging nonvolatile memory due to their high density and excellent scalability. Their ability to perform logic operations using RRAM devices makes them a critical component in non-von Neumann processing-in-memory architectures. Passive RRAM crossbar arrays (1-RRAM or 1R), however, suffer from a major issue of sneak path currents, leading to a lower readout margin and increasing write failures. To address this challenge, active RRAM arrays have been proposed, which incorporate a selector device in each memory cell (termed 1-selector-1-RRAM or 1S1R). The selector eliminates currents from unselected cells and therefore effectively mitigates the sneak path phenomenon. Yet, there is a need for a comprehensive analysis of 1S1R arrays, particularly concerning in-memory computation. In this paper, we introduce a 1S1R model tailored to a VO2-based selector and TiN/TiOx/HfOx/Pt RRAM device. We also present simulations of 1S1R arrays, incorporating all parasitic parameters, across a range of array sizes from $4\times4$ to $512\times512$. We evaluate the performance of Memristor-Aided Logic (MAGIC) gates in terms of switching delay, power consumption, and readout margin, and provide a comparative evaluation with passive 1R arrays., Comment: 5 pages, 7 figures

The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, addressing material challenges, exploring novel computing concepts, and finally examining the maturity level of emerging technologies while determining the next essential steps for their advancement., Comment: 90 pages, 22 figures, roadmap, neuromorphic

Transimpedance amplifiers (TIA) play a crucial role in various electronic systems, especially in optical signal acquisition. However, their performance is often hampered by saturation issues due to high input currents, leading to prolonged recovery times. This paper addresses this challenge by introducing a novel approach utilizing a memristive automatic gain control (AGC) to adjust the TIA's gain and enhance its dynamic range. We replace the typical feedback resistor of a TIA with a valence-change mechanism (VCM) memristor. This substitution enables the TIA to adapt to a broader range of input signals, leveraging the substantial OFF/ON resistance ratio of the memristor. This paper also presents the reading and resetting sub-circuits essential for monitoring and controling the memristor's state. The proposed circuit is evaluated through SPICE simulations. Furthermore, we extend our evaluation to practical testing using a printed circuit board (PCB) integrating the TIA and memristor. We show a remarkable 40 dB increase in the dynamic range of our TIA memristor circuit compared to traditional resistor-based TIAs.

Processing-in-memory (PIM) is attractive to overcome the limitations of modern computing systems. Numerous PIM systems exist, varying by the technologies and logic techniques used. Successful operation of specific logic functions is crucial for effective processing-in-memory. Memristive non-stateful logic techniques are compatible with CMOS logic and can be integrated into a 1T1R memory array, similar to commercial RRAM products. This paper analyzes and demonstrates two non-stateful logic techniques: 1T1R logic and scouting logic. As a first step, the used 1T1R SiO\textsubscript{x} valence change mechanism memristors are characterized in reference to their feasibility to perform logic functions. Various logical functions of the two logic techniques are experimentally demonstrated, showing correct functionality in all cases. Following the results, the challenges and limitations of the RRAM characteristics and 1T1R configuration for the application in logical functions are discussed., Comment: 5 pages, 6 figures

Digital processing-in-memory (PIM) architectures mitigate the memory wall problem by facilitating parallel bitwise operations directly within the memory. Recent works have demonstrated their algorithmic potential for accelerating data-intensive applications; however, there remains a significant gap in the programming model and microarchitectural design. This is further exacerbated by aspects unique to memristive PIM such as partitions and operations across both directions of the memory array. To address this gap, this paper provides an end-to-end architectural integration of digital memristive PIM from a high-level Python library for tensor operations (similar to NumPy and PyTorch) to the low-level microarchitectural design. We begin by proposing an efficient microarchitecture and instruction set architecture (ISA) that bridge the gap between the low-level control periphery and an abstraction of PIM parallelism. We subsequently propose a PIM development library that converts high-level Python to ISA instructions and a PIM driver that translates ISA instructions into PIM micro-operations. We evaluate PyPIM via a cycle-accurate simulator on a wide variety of benchmarks that both demonstrate the versatility of the Python library and the performance compared to theoretical PIM bounds. Overall, PyPIM drastically simplifies the development of PIM applications and enables the conversion of existing tensor-oriented Python programs to PIM with ease., Comment: Accepted to the 57th IEEE/ACM International Symposium on Microarchitecture (MICRO)

Processing-in-memory (PIM) architectures are emerging to reduce data movement in data-intensive applications. These architectures seek to exploit the same physical devices for both information storage and logic, thereby dwarfing the required data transfer and utilizing the full internal memory bandwidth. Whereas analog PIM utilizes the inherent connectivity of crossbar arrays for approximate matrix-vector multiplication in the analog domain, digital PIM architectures enable bitwise logic operations with massive parallelism across columns of data within memory arrays. Several recent works have extended the computational capabilities of digital PIM architectures towards the full-precision (single-precision floating-point) acceleration of convolutional neural networks (CNNs); yet, they lack a comprehensive comparison to GPUs. In this paper, we examine the potential of digital PIM for CNN acceleration through an updated quantitative comparison with GPUs, supplemented with an analysis of the overall limitations of digital PIM. We begin by investigating the different PIM architectures from a theoretical perspective to understand the underlying performance limitations and improvements compared to state-of-the-art hardware. We then uncover the tradeoffs between the different strategies through a series of benchmarks ranging from memory-bound vectored arithmetic to CNN acceleration. We conclude with insights into the general performance of digital PIM architectures for different data-intensive applications.

The Discrete Fourier Transform (DFT) is essential for various applications ranging from signal processing to convolution and polynomial multiplication. The groundbreaking Fast Fourier Transform (FFT) algorithm reduces DFT time complexity from the naive O(n^2) to O(n log n), and recent works have sought further acceleration through parallel architectures such as GPUs. Unfortunately, accelerators such as GPUs cannot exploit their full computing capabilities as memory access becomes the bottleneck. Therefore, this paper accelerates the FFT algorithm using digital Processing-in-Memory (PIM) architectures that shift computation into the memory by exploiting physical devices capable of storage and logic (e.g., memristors). We propose an O(log n) in-memory FFT algorithm that can also be performed in parallel across multiple arrays for high-throughput batched execution, supporting both fixed-point and floating-point numbers. Through the convolution theorem, we extend this algorithm to O(log n) polynomial multiplication - a fundamental task for applications such as cryptography. We evaluate FourierPIM on a publicly-available cycle-accurate simulator that verifies both correctness and performance, and demonstrate 5-15x throughput and 4-13x energy improvement over the NVIDIA cuFFT library on state-of-the-art GPUs for FFT and polynomial multiplication.

Bulk-bitwise processing-in-memory (PIM), an emerging computational paradigm utilizing memory arrays as computational units, has been shown to benefit database applications. This paper demonstrates how GROUP-BY and JOIN, database operations not supported by previous works, can be performed efficiently in bulk-bitwise PIM for relational database analytical processing. We extend the gem5 simulator and evaluated our hardware modifications on the Star Schema Benchmark. We show that compared to previous works, our modifications improve (on average) execution time by 1.83X, energy by 4.31X, and the system's lifetime by 3.21X. We also achieved a speedup of 4.65X over MonetDB, a modern state-of-the-art in-memory database., Comment: Included in conference proceedings of the 2023 IEEE 36th International System-on-Chip Conference (SOCC)

Processing-in-memory (PIM) architectures allow software to explicitly initiate computation in the memory. This effectively makes PIM operations a new class of memory operations, alongside standard memory operations (e.g., load, store). For software correctness, it is crucial to have ordering rules for a PIM operation with other PIM operations and other memory operations, i.e., a consistency model that takes into account PIM operations is vital. To the best of our knowledge, little attention to PIM operation consistency has been given in existing works. In this paper, we focus on a specific PIM approach, named bulk-bitwise PIM. In bulk-bitwise PIM, large bitwise operations are performed directly and stored in the memory array. We show that previous solutions for the related topic of maintaining coherency of bulk-bitwise PIM have broken the host native consistency model and prevent any guaranteed correctness. As a solution, we propose and evaluate four consistency models for bulk-bitwise PIM, from strict to relaxed. Our designs also preserve coherency between PIM and the host processor. Evaluating the proposed designs' performance with a gem5 simulation, using the YCSB short-range scan benchmark and TPC-H queries, shows that the run time overhead of guaranteeing correctness is at most $6\%$, and in many cases the run time is even improved. The hardware overhead of our design is less than $0.22\%$., Comment: To be published in the 29th IEEE International Symposium on High-Performance Computer Architecture (HPCA-29)

The emerging memristive Memory Processing Unit (mMPU) overcomes the memory wall through memristive devices that unite storage and logic for real processing-in-memory (PIM) systems. At the core of the mMPU is stateful logic, which is accelerated with memristive partitions to enable logic with massive inherent parallelism within crossbar arrays. This paper vastly accelerates the fundamental operations of matrix-vector multiplication and convolution in the mMPU, with either full-precision or binary elements. These proposed algorithms establish an efficient foundation for large-scale mMPU applications such as neural-networks, image processing, and numerical methods. We overcome the inherent asymmetry limitation in the previous in-memory full-precision matrix-vector multiplication solutions by utilizing techniques from block matrix multiplication and reduction. We present the first fast in-memory binary matrix-vector multiplication algorithm by utilizing memristive partitions with a tree-based popcount reduction (39x faster than previous work). For convolution, we present a novel in-memory input-parallel concept which we utilize for a full-precision algorithm that overcomes the asymmetry limitation in convolution, while also improving latency (2x faster than previous work), and the first fast binary algorithm (12x faster than previous work).

Digital processing-in-memory (PIM) architectures are rapidly emerging to overcome the memory-wall bottleneck by integrating logic within memory elements. Such architectures provide vast computational power within the memory itself in the form of parallel bitwise logic operations. We develop novel algorithmic techniques for PIM that, combined with new perspectives on computer arithmetic, extend this bitwise parallelism to the four fundamental arithmetic operations (addition, subtraction, multiplication, and division), for both fixed-point and floating-point numbers, and using both bit-serial and bit-parallel approaches. We propose a state-of-the-art suite of arithmetic algorithms, demonstrating the first algorithm in the literature of digital PIM for a majority of cases - including cases previously considered impossible for digital PIM, such as floating-point addition. Through a case study on memristive PIM, we compare the proposed algorithms to an NVIDIA RTX 3070 GPU and demonstrate significant throughput and energy improvements., Comment: Accepted to IEEE Transactions on Emerging Topics in Computing (TETC)

Digital memristive processing-in-memory overcomes the memory wall through a fundamental storage device capable of stateful logic within crossbar arrays. Dynamically dividing the crossbar arrays by adding memristive partitions further increases parallelism, thereby overcoming an inherent trade-off in memristive processing-in-memory. The algorithmic topology of partitions is highly unique, and was recently exploited to accelerate multiplication (11x with 32 partitions) and sorting (14x with 16 partitions). Yet, the physical implementation of memristive partitions, such as the peripheral decoders and the control message, has never been considered and may lead to vast impracticality. This paper overcomes that challenge with several novel techniques, presenting efficient practical designs of memristive partitions. We begin by formalizing the algorithmic properties of memristive partitions into serial, parallel, and semi-parallel operations. Peripheral overhead is addressed via a novel technique of half-gates that enables efficient decoding with negligible overhead. Control overhead is addressed by carefully reducing the operation set of memristive partitions, while resulting in negligible performance impact, by utilizing techniques such as shared indices and pattern generators. Ultimately, these efficient practical solutions, combined with the vast algorithmic potential, may revolutionize digital memristive processing-in-memory.

Memristive technologies are attractive candidates to replace conventional memory technologies, and can also be used to perform logic and arithmetic operations using a technique called 'stateful logic.' Combining data storage and computation in the memory array enables a novel non-von Neumann architecture, where both the operations are performed within a memristive Memory Processing Unit (mMPU). The mMPU relies on adding computing capabilities to the memristive memory cells without changing the basic memory array structure. The use of an mMPU alleviates the primary restriction on performance and energy in a von Neumann machine, which is the data transfer between CPU and memory. Here, the various aspects of mMPU are discussed, including its architecture and implications on the computing system and software, as well as examining the microarchitectural aspects. We show how mMPU can be improved to accelerate different applications and how the poor reliability of memristors can be improved as part of the mMPU operation.

Recent research has sought to accelerate cryptographic hash functions as they are at the core of modern cryptography. Traditional designs, however, suffer from the von Neumann bottleneck that originates from the separation of processing and memory units. An emerging solution to overcome this bottleneck is processing-in-memory (PIM): performing logic within the same devices responsible for memory to eliminate data-transfer and simultaneously provide massive computational parallelism. In this paper, we seek to vastly accelerate the state-of-the-art SHA-3 cryptographic function using the memristive memory processing unit (mMPU), a general-purpose memristive PIM architecture. To that end, we propose a novel in-memory algorithm for variable rotation, and utilize an efficient mapping of the SHA-3 state vector for memristive crossbar arrays to efficiently exploit PIM parallelism. We demonstrate a massive energy efficiency of 1,422 Gbps/W, improving a state-of-the-art memristive SHA-3 accelerator (SHINE-2) by 4.6x., Comment: Accepted to International Conference on Modern Circuits and Systems Technologies (MOCAST) 2022

Bulk-bitwise processing-in-memory (PIM), where large bitwise operations are performed in parallel by the memory array itself, is an emerging form of computation with the potential to mitigate the memory wall problem. This paper examines the capabilities of bulk-bitwise PIM by constructing PIMDB, a fully-digital system based on memristive stateful logic, utilizing and focusing on in-memory bulk-bitwise operations, designed to accelerate a real-life workload: analytical processing of relational databases. We introduce a host processor programming model to support bulk-bitwise PIM in virtual memory, develop techniques to efficiently perform in-memory filtering and aggregation operations, and adapt the application data set into the memory. To understand bulk-bitwise PIM, we compare it to an equivalent in-memory database on the same host system. We show that bulk-bitwise PIM substantially lowers the number of required memory read operations, thus accelerating TPC-H filter operations by 1.6$\times$--18$\times$ and full queries by 56$\times$--608$\times$, while reducing the energy consumption by 1.7$\times$--18.6$\times$ and 0.81$\times$--12$\times$ for these benchmarks, respectively. Our extensive evaluation uses the gem5 full-system simulation environment. The simulations also evaluate cell endurance, showing that the required endurance is within the range of existing endurance of RRAM devices., Comment: published in IEEE Transactions on Emerging Topics in Computing

Processing-in-memory (PIM) solutions vastly accelerate systems by reducing data transfer between computation and memory. Memristors possess a unique property that enables storage and logic within the same device, which is exploited in the memristive Memory Processing Unit (mMPU). The mMPU expands fundamental stateful logic techniques, such as IMPLY, MAGIC and FELIX, to high-throughput parallel logic and arithmetic operations within the memory. Unfortunately, memristive processing-in-memory is highly vulnerable to soft errors and this massive parallelism is not compatible with traditional reliability techniques, such as error-correcting-code (ECC). In this paper, we discuss reliability techniques that efficiently support the mMPU by utilizing the same principles as the mMPU computation. We detail ECC techniques that are based on the unique properties of the mMPU to efficiently utilize the massive parallelism. Furthermore, we present novel solutions for efficiently implementing triple modular redundancy (TMR). The short-term and long-term reliability of large-scale applications, such as neural-network acceleration, are evaluated. The analysis clearly demonstrates the importance of high-throughput reliability mechanisms for memristive processing-in-memory., Comment: Accepted to 28th International Conference on Electronics Circuits and Systems (ICECS) 2021

Processing-in-memory (PIM) seeks to eliminate computation/memory data transfer using devices that support both storage and logic. Stateful logic techniques such as IMPLY, MAGIC and FELIX can perform logic gates within memristive crossbar arrays with massive parallelism. Multiplication via stateful logic is an active field of research due to the wide implications. Recently, RIME has become the state-of-the-art algorithm for stateful single-row multiplication by using memristive partitions, reducing the latency of the previous state-of-the-art by 5.1x. In this paper, we begin by proposing novel partition-based computation techniques for broadcasting and shifting data. Then, we design an in-memory multiplication algorithm based on the carry-save add-shift (CSAS) technique. Finally, we develop a novel stateful full-adder that significantly improves the state-of-the-art (FELIX) design. These contributions constitute MultPIM, a multiplier that reduces state-of-the-art time complexity from quadratic to linear-log. For 32-bit numbers, MultPIM improves latency by an additional 4.2x over RIME, while even slightly reducing area overhead. Furthermore, we optimize MultPIM for full-precision matrix-vector multiplication and improve latency by 25.5x over FloatPIM matrix-vector multiplication., Comment: Accepted to IEEE Transactions On Circuits And Systems-II (TCAS-II)

Nowadays, data-intensive applications are gaining popularity and, together with this trend, processing-in-memory (PIM)-based systems are being given more attention and have become more relevant. This paper describes an analytical modeling tool called Bitlet that can be used, in a parameterized fashion, to estimate the performance and the power/energy of a PIM-based system and thereby assess the affinity of workloads for PIM as opposed to traditional computing. The tool uncovers interesting tradeoffs between, mainly, the PIM computation complexity (cycles required to perform a computation through PIM), the amount of memory used for PIM, the system memory bandwidth, and the data transfer size. Despite its simplicity, the model reveals new insights when applied to real-life examples. The model is demonstrated for several synthetic examples and then applied to explore the influence of different parameters on two systems - IMAGING and FloatPIM. Based on the demonstrations, insights about PIM and its combination with CPU are concluded., Comment: Accepted to ACM JETC

Inefficient data transfer between computation and memory inspired emerging processing-in-memory (PIM) technologies. Many PIM solutions enable storage and processing using memristors in a crossbar-array structure, with techniques such as memristor-aided logic (MAGIC) used for computation. This approach provides highly-paralleled logic computation with minimal data movement. However, memristors are vulnerable to soft errors and standard error-correcting-code (ECC) techniques are difficult to implement without moving data outside the memory. We propose a novel technique for efficient ECC implementation along diagonals to support reliable computation inside the memory without explicitly reading the data. Our evaluation demonstrates an improvement of over eight orders of magnitude in reliability (mean time to failure) for an increase of about 26% in computation latency., Comment: Accepted to 58th Design Automation Conference (DAC) 2021

Data-intensive applications are poised to benefit directly from processing-in-memory platforms, such as memristive Memory Processing Units, which allow leveraging data locality and performing stateful logic operations. Developing design automation flows for such platforms is a challenging and highly relevant research problem. In this work, we investigate the problem of minimizing delay under arbitrary area constraint for MAGIC-based in-memory computing platforms. We propose an end-to-end area constrained technology mapping framework, CONTRA. CONTRA uses Look-Up Table(LUT) based mapping of the input function on the crossbar array to maximize parallel operations and uses a novel search technique to move data optimally inside the array. CONTRA supports benchmarks in a variety of formats, along with crossbar dimensions as input to generate MAGIC instructions. CONTRA scales for large benchmarks, as demonstrated by our experiments. CONTRA allows mapping benchmarks to smaller crossbar dimensions than achieved by any other technique before, while allowing a wide variety of area-delay trade-offs. CONTRA improves the composite metric of area-delay product by 2.1x to 13.1x compared to seven existing technology mapping approaches., Comment: 9 pages

Quantized neural networks (QNNs) are being actively researched as a solution for the computational complexity and memory intensity of deep neural networks. This has sparked efforts to develop algorithms that support both inference and training with quantized weight and activation values, without sacrificing accuracy. A recent example is the GXNOR framework for stochastic training of ternary (TNN) and binary (BNN) neural networks. In this paper, we show how magnetic tunnel junction (MTJ) devices can be used to support QNN training. We introduce a novel hardware synapse circuit that uses the MTJ stochastic behavior to support the quantize update. The proposed circuit enables processing near memory (PNM) of QNN training, which subsequently reduces data movement. We simulated MTJ-based stochastic training of a TNN over the MNIST, SVHN, and CIFAR10 datasets and achieved an accuracy of 98.61%, 93.99% and 82.71%, respectively (less than 1% degradation compared to the GXNOR algorithm). We evaluated the synapse array performance potential and showed that the proposed synapse circuit can train ternary networks in situ, with 18.3TOPs/W for feedforward and 3TOPs/W for weight update., Comment: Published in Semiconductor Science and Technology, Vol 36

This paper describes an analytical modeling tool called Bitlet that can be used, in a parameterized fashion, to understand the affinity of workloads to processing-in-memory (PIM) as opposed to traditional computing. The tool uncovers interesting trade-offs between operation complexity (cycles required to perform an operation through PIM) and other key parameters, such as system memory bandwidth, data transfer size, the extent of data alignment, and effective memory capacity involved in PIM computations. Despite its simplicity, the model has already proven useful. In the future, we intend to extend and refine Bitlet to further increase its utility.

The key challenge to improving performance in the age of Dark Silicon is how to leverage transistors when they cannot all be used at the same time. In modern SOCs, these transistors are often used to create specialized accelerators which improve energy efficiency for some applications by 10-1000X. While this might seem like the magic bullet we need, for most CPU applications more energy is dissipated in the memory system than in the processor: these large gains in efficiency are only possible if the DRAM and memory hierarchy are mostly idle. We refer to this desirable state as Dark Memory, and it only occurs for applications with an extreme form of locality. To show our findings, we introduce Pareto curves in the energy/op and mm$^2$/(ops/s) metric space for compute units, accelerators, and on-chip memory/interconnect. These Pareto curves allow us to solve the power, performance, area constrained optimization problem to determine which accelerators should be used, and how to set their design parameters to optimize the system. This analysis shows that memory accesses create a floor to the achievable energy-per-op. Thus high performance requires Dark Memory, which in turn requires co-design of the algorithm for parallelism and locality, with the hardware., Comment: 8 pages, To appear in IEEE Design and Test Journal