AIMC Standardized Evaluation Model: Unraveling the intricacies of Analog In-Memory Computing
Abstract
Analog In-Memory Computing (AIMC) is a promising paradigm that marries analog computation with non-volatile memory (NVM). This unique architecture offers tantalizing advantages such as ultra-low power consumption, blazing-fast processing speeds, and inherent parallelism. However, realizing the full potential of AIMC hinges upon comprehensive evaluation methodologies that faithfully capture its nuances. This article introduces a standardized evaluation model for AIMC that meticulously incorporates realistic hardware constraints, enabling researchers and practitioners to accurately assess the performance and limitations of their AIMC designs.
Analog Crossbar Model: Delving into the Heart of AIMC
At the core of AIMC lies the analog crossbar, an array of non-volatile memory cells capable of performing matrix-vector multiplication (MVM) operations. Our standardized evaluation model meticulously emulates the behavior of analog crossbars, encompassing finite dynamic range, weight drift, read noise, and short-term noise effects. This comprehensive approach ensures that the model accurately reflects the intricacies of real-world AIMC hardware.
Weight Programming and Drift: Capturing the Dynamics of NVM
To accurately model the evolution of weights over time, we leverage empirical models for weight programming and drift. These models capture the gradual conductance changes in NVM devices due to post-programming relaxation and low-frequency noise. By incorporating these effects, our evaluation model provides a realistic assessment of AIMC’s performance under varying conditions.
Crossbar Tile Size and IR-Drop: Addressing Practical Considerations
In real-world AIMC implementations, crossbars are of finite size, and IR-drop effects can significantly impact the accuracy of MVM operations. Our standardized evaluation model takes these practical considerations into account, allowing researchers to explore the trade-offs between crossbar size, accuracy, and power consumption.
Additional Nonlinearities: Embracing Imperfections
Emerging NVM devices exhibit inherent nonlinearities, such as imperfect yield, S-shaped ADC output nonlinearity, and polarity-dependent conductance. Our evaluation model incorporates these additional nonlinearities, providing a holistic assessment of AIMC’s robustness to hardware imperfections.
MVM Error Calculation: Quantifying the Fidelity of Analog Computation
To quantify the accuracy of analog MVM operations, we introduce the MVM error metric. This metric measures the relative deviation between the analog MVM results and the ideal MVM results, providing a comprehensive assessment of the impact of hardware nonidealities on computation accuracy.
AIMC Hardware-Aware DNN Training: Mitigating the Impact of Nonidealities
AIMC hardware-aware (HWA) training is a powerful technique that enhances the robustness of deep neural networks (DNNs) to hardware nonidealities. Our standardized evaluation model seamlessly integrates HWA training, enabling researchers to explore various noise injection methods, learn weight-to-conductance conversion factors, and optimize input range clipping.
Conclusion: Paving the Way for Reliable AIMC Implementations
The standardized evaluation model presented in this article provides a comprehensive framework for assessing the performance and limitations of AIMC designs. By faithfully capturing the nuances of analog crossbars, weight drift, crossbar size, IR-drop, and additional nonlinearities, this model empowers researchers and practitioners to make informed decisions about AIMC architectures, algorithms, and training methodologies. As AIMC continues to gain traction, this standardized evaluation model will play a pivotal role in accelerating the development of reliable and efficient AIMC systems.