Advances in Compute-in-Memory Architectures

The field of compute-in-memory (CIM) architectures is rapidly advancing, with a focus on addressing the 'memory wall' bottleneck and improving the efficiency of deep neural network (DNN) acceleration. Recent developments have led to the creation of integrated frameworks that provide a comprehensive workflow for implementing and evaluating DNN workloads on digital CIM architectures. These frameworks enable systematic prototyping and optimization of digital CIM architectures, allowing researchers and designers to explore a wide range of design spaces. Additionally, novel compilation techniques have been proposed to explore a larger search space of programs, reducing memory requirements and improving throughput. The use of analog computing-in-memory (ACIM) is also being explored, with advances in modeling device- and circuit-level non-idealities and improved software backbones for benchmarking ACIM accelerators. Noteworthy papers include:

  • CIMFlow, which provides an integrated framework for systematic design and evaluation of digital CIM architectures.
  • Morello, which introduces a dynamic-programming-based approach to compile fast neural networks with improved throughput.
  • NeuroSim V1.5, which advances the design and validation of next-generation ACIM accelerators with improved modeling of device- and circuit-level non-idealities.
  • PUDTune, which presents a novel high-precision calibration technique for increasing the number of error-free columns in processing-using-DRAM.

Sources

CIMFlow: An Integrated Framework for Systematic Design and Evaluation of Digital CIM Architectures

Morello: Compiling Fast Neural Networks with Dynamic Programming and Spatial Compression

NeuroSim V1.5: Improved Software Backbone for Benchmarking Compute-in-Memory Accelerators with Device and Circuit-level Non-idealities

Content Addressable Memory Design with Reference Resistor for Improved Search Resolution

PUDTune: Multi-Level Charging for High-Precision Calibration in Processing-Using-DRAM

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