Energy-efficient analog-domain aggregator circuit for RRAM-based neural network accelerators
Recently, there has been notable progress in the advancement of RRAM-based Compute-In-Memory (CIM) architectures, showing promise in accelerating neural networks with remarkable energy efficiency and parallelism. However, challenges persist in fully integrating large-scale networks onto a chip, part...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Electronics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/felec.2025.1513127/full |
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| author | Khaled Humood Yihan Pan Shiwei Wang Alexander Serb Themis Prodromakis |
| author_facet | Khaled Humood Yihan Pan Shiwei Wang Alexander Serb Themis Prodromakis |
| author_sort | Khaled Humood |
| collection | DOAJ |
| description | Recently, there has been notable progress in the advancement of RRAM-based Compute-In-Memory (CIM) architectures, showing promise in accelerating neural networks with remarkable energy efficiency and parallelism. However, challenges persist in fully integrating large-scale networks onto a chip, particularly when the weights of a layer exceed the capacity of the RRAM crossbar. In such cases, weights are distributed across smaller RRAM crossbars and aggregated using tree adders and shifters in digital flow, leading to increased system complexity and energy consumption of hardware accelerators. In this work, we introduce a novel energy-efficient analog domain aggregator system designed for RRAM-based CIM systems. The proposed circuit has been verified and tested using Virtuoso Cadence circuit tools in 180 nm CMOS technology with post-layout simulations and analysis. Compared with the digital adder tree approach, the proposed analog aggregator offers improvements in three key areas: it can handle an arbitrary number of inputs not just powers of 2, achieves lower error through better rounding and improves power efficiency (2.15× lower consumption). These findings mark a substantial advancement towards the full implementation of efficient on-chip hardware accelerator systems. |
| format | Article |
| id | doaj-art-70e19363bde042288dd0cd10633b9f5b |
| institution | Kabale University |
| issn | 2673-5857 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Electronics |
| spelling | doaj-art-70e19363bde042288dd0cd10633b9f5b2025-02-04T06:32:05ZengFrontiers Media S.A.Frontiers in Electronics2673-58572025-02-01610.3389/felec.2025.15131271513127Energy-efficient analog-domain aggregator circuit for RRAM-based neural network acceleratorsKhaled HumoodYihan PanShiwei WangAlexander SerbThemis ProdromakisRecently, there has been notable progress in the advancement of RRAM-based Compute-In-Memory (CIM) architectures, showing promise in accelerating neural networks with remarkable energy efficiency and parallelism. However, challenges persist in fully integrating large-scale networks onto a chip, particularly when the weights of a layer exceed the capacity of the RRAM crossbar. In such cases, weights are distributed across smaller RRAM crossbars and aggregated using tree adders and shifters in digital flow, leading to increased system complexity and energy consumption of hardware accelerators. In this work, we introduce a novel energy-efficient analog domain aggregator system designed for RRAM-based CIM systems. The proposed circuit has been verified and tested using Virtuoso Cadence circuit tools in 180 nm CMOS technology with post-layout simulations and analysis. Compared with the digital adder tree approach, the proposed analog aggregator offers improvements in three key areas: it can handle an arbitrary number of inputs not just powers of 2, achieves lower error through better rounding and improves power efficiency (2.15× lower consumption). These findings mark a substantial advancement towards the full implementation of efficient on-chip hardware accelerator systems.https://www.frontiersin.org/articles/10.3389/felec.2025.1513127/fullin-memory-computingANNacceleratorsanalog-computingaggregatoraccumulator |
| spellingShingle | Khaled Humood Yihan Pan Shiwei Wang Alexander Serb Themis Prodromakis Energy-efficient analog-domain aggregator circuit for RRAM-based neural network accelerators Frontiers in Electronics in-memory-computing ANN accelerators analog-computing aggregator accumulator |
| title | Energy-efficient analog-domain aggregator circuit for RRAM-based neural network accelerators |
| title_full | Energy-efficient analog-domain aggregator circuit for RRAM-based neural network accelerators |
| title_fullStr | Energy-efficient analog-domain aggregator circuit for RRAM-based neural network accelerators |
| title_full_unstemmed | Energy-efficient analog-domain aggregator circuit for RRAM-based neural network accelerators |
| title_short | Energy-efficient analog-domain aggregator circuit for RRAM-based neural network accelerators |
| title_sort | energy efficient analog domain aggregator circuit for rram based neural network accelerators |
| topic | in-memory-computing ANN accelerators analog-computing aggregator accumulator |
| url | https://www.frontiersin.org/articles/10.3389/felec.2025.1513127/full |
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