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In-Memory Computing Based Hardware Accelerators Advancing the Implementation of AI/ML Tasks in Edge Devices.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	In-Memory Computing Based Hardware Accelerators Advancing the Implementation of AI/ML Tasks in Edge Devices./
Author:	Saikia, Jyotishman.
Description:	1 online resource (105 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798381156751

In-Memory Computing Based Hardware Accelerators Advancing the Implementation of AI/ML Tasks in Edge Devices.
Saikia, Jyotishman.

In-Memory Computing Based Hardware Accelerators Advancing the Implementation of AI/ML Tasks in Edge Devices. - 1 online resource (105 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--Arizona State University, 2023.

Includes bibliographical references

Artificial Intelligence (AI) and Machine Learning (ML) techniques have come a long way since their inception and have been used to build intelligent systems for a wide range of applications in everyday life. However they are very computationintensive and require transfer of large volume of data from memory to the computation units. This memory access time constitute significant part of the computational latency and a performance bottleneck. To address this limitation and the ever-growing demand for implementation in hand-held and edge-devices, In-memory computing (IMC) based AI/ML hardware accelerators have emerged.First, the dissertation presents an IMC static random access memory (SRAM) based hardware modeling and optimization framework. A unified systematic study closely models the IMC hardware, and investigates how a number of design variables and non-idealities (e.g. device mismatch and ADC quantization) affect the Deep Neural Network (DNN) accuracy of the IMC design. The framework allows co-optimized selection of different design variables accounting for sources of noise in IMC hardware and robust implementation of a high accuracy DNN.Next, it presents a kNN hardware accelerator in 65nm Complementary Metal-Oxide-Semiconductor (CMOS) technology. The accelerator combines an IMC SRAM that is developed for binarized deep neural networks and other digital hardware that performs top-k sorting. The simulated k Nearest Neighbor accelerator design processes up to 17.9 million query vectors per second while consuming 11.8 mW, demonstrating >4.8x energy-efficiency improvement over prior works.This dissertation also presents a novel floating-point precision IMC (FP-IMC) macro with a hybrid architecture that configurably supports two Floating Point (FP) precisions. Implementing FP precision MAC has been a challenge owing to its complexity. The design is implemented on 28nm CMOS, and taped-out on chip demonstrating 12.1 TFLOPS/W and 66.1 TFLOPS/W for 8-bit Floating Point (FP8) and Block Floating point (BF8) respectively.Finally, another iteration of the FP design is presented that is modeled to support multiple precision modes from FP8 up to FP32. Two approaches to the architectural design were compared illustrating the throughput-area overhead trade-off. The simulated design shows a 2.1 x normalized energy-efficiency compared to the on-chip implementation of the FP-IMC.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381156751Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Deep Neural Networks (DNN)Index Terms--Genre/Form:

554714
Electronic books.

In-Memory Computing Based Hardware Accelerators Advancing the Implementation of AI/ML Tasks in Edge Devices.
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520 $a Artificial Intelligence (AI) and Machine Learning (ML) techniques have come a long way since their inception and have been used to build intelligent systems for a wide range of applications in everyday life. However they are very computationintensive and require transfer of large volume of data from memory to the computation units. This memory access time constitute significant part of the computational latency and a performance bottleneck. To address this limitation and the ever-growing demand for implementation in hand-held and edge-devices, In-memory computing (IMC) based AI/ML hardware accelerators have emerged.First, the dissertation presents an IMC static random access memory (SRAM) based hardware modeling and optimization framework. A unified systematic study closely models the IMC hardware, and investigates how a number of design variables and non-idealities (e.g. device mismatch and ADC quantization) affect the Deep Neural Network (DNN) accuracy of the IMC design. The framework allows co-optimized selection of different design variables accounting for sources of noise in IMC hardware and robust implementation of a high accuracy DNN.Next, it presents a kNN hardware accelerator in 65nm Complementary Metal-Oxide-Semiconductor (CMOS) technology. The accelerator combines an IMC SRAM that is developed for binarized deep neural networks and other digital hardware that performs top-k sorting. The simulated k Nearest Neighbor accelerator design processes up to 17.9 million query vectors per second while consuming 11.8 mW, demonstrating >4.8x energy-efficiency improvement over prior works.This dissertation also presents a novel floating-point precision IMC (FP-IMC) macro with a hybrid architecture that configurably supports two Floating Point (FP) precisions. Implementing FP precision MAC has been a challenge owing to its complexity. The design is implemented on 28nm CMOS, and taped-out on chip demonstrating 12.1 TFLOPS/W and 66.1 TFLOPS/W for 8-bit Floating Point (FP8) and Block Floating point (BF8) respectively.Finally, another iteration of the FP design is presented that is modeled to support multiple precision modes from FP8 up to FP32. Two approaches to the architectural design were compared illustrating the throughput-area overhead trade-off. The simulated design shows a 2.1 x normalized energy-efficiency compared to the on-chip implementation of the FP-IMC.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
653 $a Deep Neural Networks (DNN)
653 $a Edge-devices
653 $a In-memory computing (IMC)
653 $a Machine Learning (ML) tasks
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710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Arizona State University. $b Electrical Engineering. $3 845389
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