Accurate and Energy-Efficient Bit-Slicing for RRAM-Based Neural Networks

Sumit Diware*, Abhairaj Singh, Anteneh Gebregiorgis, Rajiv V. Joshi, Said Hamdioui, Rajendra Bishnoi

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
28 Downloads (Pure)


Computation-in-memory (CIM) paradigm leverages emerging memory technologies such as resistive random access memories (RRAMs) to process the data within the memory itself. This alleviates the memory-processor bottleneck resulting in much higher hardware efficiency compared to von-Neumann architecture-based conventional hardware. Hence, CIM becomes an attractive alternative for applications like neural networks which require a huge number of data transfer operations in conventional hardware. CIM-based neural networks typically employ bit-slicing scheme which represents a single neural weight using multiple RRAM devices (called slices) to meet the high bit-precision demand. However, such neural networks suffer from significant accuracy degradation due to non-zero Gmin error where a zero weight in the neural network is represented by an RRAM device with a non-zero conductance. This paper proposes an unbalanced bit-slicing scheme to mitigate the impact of non-zero Gmin error. It achieves this by allocating appropriate sensing margins for different slices based on their binary positions. It also tunes the sensing margins to meet the demands of either high accuracy or energy-efficiency. The sensing margin allocation is supported by 2's complement arithmetic which further reduces the influence of non-zero Gmin error. Simulation results show that our proposed scheme achieves up to 7.3× accuracy and up to 7.8× correct operations per unit energy consumption compared to state-of-the-art.

Original languageEnglish
Article number9840507
Pages (from-to)164 - 177
Number of pages14
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number1
Publication statusPublished - 2023

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • Computation-in-memory
  • bit-slicing
  • neural networks
  • non-zero Gmin error
  • conductance variation
  • nonidealities


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