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In Memory Computing (IMC) is one of the most promising candidates
for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is principal component analysis (PCA), which heavily relies on matrixvector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental
demonstration of a new IMCbased PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching random-access memory (RRAM). The classification accuracy of the Wisconsin Breast Cancer data set reaches 95.43%, close to floatingpoint implementation. Our simulations indicate a 250× improvement in energy efficiency compared to commercial GPUs, thus supporting IMC for energy-efficient ML in modern data-intensive computing.
RRAM technology is a promising candidate for implementing efficient AI accelerators with extensive multiply-accumulate operations. By scaling RRAM devices to the synaptic crossbar array, the computations can be realized in situ, avoiding frequent weights transfer between the processing units and memory. Besides, as the computations are conducted in the analog domain with high flexibility, applying multilevel input voltages to the RRAM devices with multilevel conductance states enhances the computational efficiency further. However, several non-idealities existing in emerging RRAM technology may degrade the reliability of the system. In this paper, we measured and investigated the impact of read disturb on RRAM devices with different input voltages, which incurs conductance drifts and introduces errors. The measured data are deployed to simulate the RRAM based AI inference engines with multilevel states.
Resistive Random Access Memory (RRAM) technology holds promises to improve the Field Programmable Gate Array (FPGA) performance, reduce the area footprint, and dramatically lower run-time energy requirements compared to the state-of-the-art CMOS-based products. However, the integration of RRAM in FPGAs is hindered by the high programming power consumption and by non-ideal behaviors of the device due to its stochastic nature that may overshadow the benefits in normal operation mode. To cope with these challenges, optimized programming strategies have to be investigated. In this work, we explore the impact that different procedures to set the device have on the run-time performance. Process, voltage, and temperature (PVT) variations as well as time-dependent drift effect of the RRAM device are considered in the assessment of 4T1R MUX designs characteristics. The comparison with tradition CMOS implementations reveals how the choice of the target resistive state and the programming algorithm are key design aspects to reduce the run-time delay and energy metrics, while at the same time improving the robustness against the different sources of variations.
AbstractBlooming and pruning is one of the most important developmental mechanisms of the biological brain in the first years of life, enabling it to adapt its network structure to the demands of the environment. The mechanism is thought to be fundamental for the development of cognitive skills. Inspired by this, Chialvo and Bak proposed in 1999 a learning scheme that learns from mistakes by eliminating from the initial surplus of synaptic connections those that lead to an undesirable outcome. Here, this idea is implemented in a neuromorphic circuit scheme using CMOS integrated HfO2-based memristive devices. The implemented two-layer neural network learns in a self-organized manner without positive reinforcement and exploits the inherent variability of the memristive devices. This approach provides hardware, local, and energy-efficient learning. A combined experimental and simulation-based parameter study is presented to find the relevant system and device parameters leading to a compact and robust memristive neuromorphic circuit that can handle association tasks.
One of the objectives fostered in medical science is the so-called precision medicine, which requires the analysis of a large amount of survival data from patients to deeply understand treatment options. Tools like Machine Learning and Deep Neural Networks are becoming a de-facto standard. Nowadays, computing facilities based on the Von Neumann architecture are devoted to these tasks, yet rapidly hitting a bottleneck in performance and energy efficiency. The In-Memory Computing (IMC) architecture emerged as a revolutionary approach to overcome that issue. In this work, we propose an IMC architecture based on Resistive switching memory (RRAM) crossbar arrays to provide a convenient primitive for matrix–vector multiplication in a single computational step. This opens massive performance improvement in the acceleration of a neural network that is frequently used in survival analysis of biomedical records, namely the DeepSurv. We explored how the synaptic weights mapping strategy and the programming algorithms developed to counter RRAM non-idealities expose a performance/energy trade-off. Finally, we assessed the benefits of the proposed architectures with respect to a GPU-based realization of the same task, evidencing a tenfold improvement in terms of performance and three orders of magnitude with respect to energy efficiency.
End-to-end modeling of variability-aware neural networks based on resistive-switching memory arrays
(2022)
Resistive-switching random access memory (RRAM) is a promising technology that enables advanced applications in the field of in-memory computing (IMC). By operating the memory array in the analogue domain, RRAM-based IMC architectures can dramatically improve the energy efficiency of deep neural networks (DNNs). However, achieving a high inference accuracy is challenged by significant variation of RRAM conductance levels, which can be compensated by (i) advanced programming techniques and (ii) variability-aware training (VAT) algorithms. In both cases, however, detailed knowledge and accurate physics-based statistical models of RRAM are needed to develop programming and VAT methodologies. This work presents an end-to-end approach to the development of highly-accurate IMC circuits with RRAM, encompassing the device modeling, the precise programming algorithm, and the VAT simulations to maximize the DNN classification accuracy in presence of conductance variations.
Resistive-switching random access memory (RRAM) is a promising technology for in-memory computing (IMC) to accelerate training and inference of deep neural networks (DNNs). This work presents the first physics-based statistical model describing (i) multilevel RRAM device program/verify (PV) algorithms by controlled set transition, (ii) the stochastic cycle-to-cycle (C2C) and device-to-device (D2D) variations within the array, and (iii) the impact of such imprecisions on the accuracy of DNN accelerators. The model can handle the full chain from RRAM materials/device parameters to the DNN performance, thus providing a valuable tool for device/circuit codesign of hardware DNN accelerators.
The crossbar structure of Resistive-switching random access memory (RRAM) arrays enabled the In-Memory Computing circuits paradigm, since they imply the native acceleration of a crucial operations in this scenario, namely the Matrix-Vector-Multiplication (MVM). However, RRAM arrays are affected by several issues materializing in conductance variations that might cause severe performance degradation. A critical
one is related to the drift of the low conductance states appearing
immediately at the end of program and verify algorithms that
are mandatory for an accurate multi-level conductance operation. In this work, we analyze the benefits of a new programming
algorithm that embodies Set and Reset switching operations to
achieve better conductance control and lower variability. Data
retention analysis performed with different temperatures for 168
hours evidence its superior performance with respect to standard programming approach. Finally, we explored the benefits
of using our methodology at a higher abstraction level, through
the simulation of an Artificial Neural Network for image recognition task (MNIST dataset). The accuracy achieved shows higher
performance stability over temperature and time.
Resistive switching memory (RRAM) is a promising technology for highly efficient computing scenarios. RRAM arrays enabled the acceleration of neural networks for artificial intelligence and the creation of In-Memory Computing circuits. However, the arrays are affected by several issues materializing in conductance variations that might cause severe performance degradation in those applications. Among those, one is related to the drift of the low conductance states appearing immediately at the end of program and verify algorithms that are fundamental for an accurate Multi-level conductance operation. In this work, we tackle the issue by developing an Incremental Reset and Verify technique showing enhanced variability and reliability features compared with a traditional refresh-based approach.
In-memory computing with resistive-switching random access memory (RRAM) crossbar arrays has the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. However, RRAM devices are subject to several non-idealities that result in significant inference accuracy drops compared with software baseline accuracy. A critical one is related to the drift of the conductance states appearing immediately at the end of program and verify algorithms that are mandatory for accurate
multi-level conductance operation. The support of drift models
in state-of-the-art simulation tools of memristive computationin-memory is currently only in the early stage, since they
overlook key device- and array-level parameters affecting drift
resilience such as the programming algorithm of RRAM cells,
the choice of target conductance states and the weight-toconductance mapping scheme. The goal of this paper is to fully expose these parameters to RRAM crossbar designers as a multi-dimensional optimization space of drift resilience. For this purpose, a simulation framework is developed, which comes with the suitable abstractions to propagate the effects of those RRAM crossbar configuration parameters to their ultimate implications over inference performance stability.