### Abstract

Original language | English |
---|---|

Title of host publication | 16th ACM International Conference on Computing Frontiers |

Publisher | Association for Computing Machinery (ACM) |

Pages | 358-365 |

ISBN (Print) | 978-1-4503-6685-4 |

DOIs | |

Publication status | Published - 19 May 2019 |

Event | 16th ACM International Conference on Computing Frontiers 2019 - Alghero, Italy Duration: 30 Apr 2019 → 2 May 2019 Conference number: 16 http://www.computingfrontiers.org/2019/ |

### Conference

Conference | 16th ACM International Conference on Computing Frontiers 2019 |
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Abbreviated title | CF 2019 |

Country | Italy |

City | Alghero |

Period | 30/04/19 → 2/05/19 |

Internet address |

### Fingerprint

### Keywords

- Least squares accelerator
- iterative workloads
- Approximate Computing
- Energy Efficiency
- Radio astronomy

### Cite this

*16th ACM International Conference on Computing Frontiers*(pp. 358-365). Association for Computing Machinery (ACM). https://doi.org/10.1145/3310273.3323161

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*16th ACM International Conference on Computing Frontiers.*Association for Computing Machinery (ACM), pp. 358-365, 16th ACM International Conference on Computing Frontiers 2019, Alghero, Italy, 30/04/19. https://doi.org/10.1145/3310273.3323161

**Energy-Efficient Approximate Least Squares Accelerator : A Case Study of Radio Astronomy Calibration Processing.** / Gillani, Syed Ghayoor Abbas; Krapukhin, Alexander; Kokkeler, Andre B.J.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Energy-Efficient Approximate Least Squares Accelerator

T2 - A Case Study of Radio Astronomy Calibration Processing

AU - Gillani, Syed Ghayoor Abbas

AU - Krapukhin, Alexander

AU - Kokkeler, Andre B.J.

PY - 2019/5/19

Y1 - 2019/5/19

N2 - Approximate computing allows the introduction of inaccuracy in the computation for cost savings, such as energy consumption, chip-area, and latency. Targeting energy efficiency, approximate designs for multipliers, adders, and multiply-accumulate (MAC) have been extensively investigated in the past decade. However, accelerator designs for relatively bigger architectures have been of less attention yet. The Least Squares (LS) algorithm is widely used in digital signal processing applications, e.g., image reconstruction. This work proposes a novel LS accelerator design based on a heterogeneous architecture, where the heterogeneity is introduced using accurate and approximate processing cores. We have considered a case study of radio astronomy calibration processing that employs a complex-input iterative LS algorithm. Our proposed methodology exploits the intrinsic error-resilience of the aforesaid algorithm, where initial iterations are processed on approximate modules while the later ones on accurate modules. Our energy-quality experiments have shown up to 24% of energy savings as compared to an accurate (optimized) counterpart for biased designs and up to 29% energy savings when unbiasing is introduced. The proposed LS accelerator design does not increase the number of iterations and provides sufficient precision to converge to an acceptable solution.

AB - Approximate computing allows the introduction of inaccuracy in the computation for cost savings, such as energy consumption, chip-area, and latency. Targeting energy efficiency, approximate designs for multipliers, adders, and multiply-accumulate (MAC) have been extensively investigated in the past decade. However, accelerator designs for relatively bigger architectures have been of less attention yet. The Least Squares (LS) algorithm is widely used in digital signal processing applications, e.g., image reconstruction. This work proposes a novel LS accelerator design based on a heterogeneous architecture, where the heterogeneity is introduced using accurate and approximate processing cores. We have considered a case study of radio astronomy calibration processing that employs a complex-input iterative LS algorithm. Our proposed methodology exploits the intrinsic error-resilience of the aforesaid algorithm, where initial iterations are processed on approximate modules while the later ones on accurate modules. Our energy-quality experiments have shown up to 24% of energy savings as compared to an accurate (optimized) counterpart for biased designs and up to 29% energy savings when unbiasing is introduced. The proposed LS accelerator design does not increase the number of iterations and provides sufficient precision to converge to an acceptable solution.

KW - Least squares accelerator

KW - iterative workloads

KW - Approximate Computing

KW - Energy Efficiency

KW - Radio astronomy

U2 - 10.1145/3310273.3323161

DO - 10.1145/3310273.3323161

M3 - Conference contribution

SN - 978-1-4503-6685-4

SP - 358

EP - 365

BT - 16th ACM International Conference on Computing Frontiers

PB - Association for Computing Machinery (ACM)

ER -