IEEE Journal of Solid-State Circuits · 2021 · 19 citations · 38 references
EngineeringComputer ArchitectureWorkload FlexibilityEmbedded SystemsArm Cortex-a53Hardware SystemsSpecialized AcceleratorsHigh-performance ArchitectureCoherent AcceleratorsComputing Systems16-Nm 25-Mm² SocEmbedded Machine LearningFlexible AccelerationInternet Of ThingsParallel ComputingPerformance ImprovementElectrical EngineeringComputer EngineeringComputer ScienceSystem On ChipHardware AccelerationDomain-specific Accelerator
Emerging Internet of Things (IoT) devices necessitate system-on-chips (SoCs) that can scale from ultralow power always-on (AON) operation, all the way up to less frequent high-performance tasks at high energy efficiency. Specialized accelerators are essential to help meet these needs at both ends of the scale, but maintaining workload flexibility remains an important goal. This article presents a 25-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> SoC in 16-nm FinFET technology which demonstrates targeted, flexible acceleration of key compute-intensive kernels spanning machine learning (ML), DSP, and cryptography. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SMIV</i> SoC includes a dedicated AON sub-system, a dual-core Arm Cortex-A53 CPU cluster, an SoC-attached embedded field-programmable gate array (eFPGA) array, and a quad-core cache-coherent accelerator (CCA) cluster. Measurement results demonstrate: 1) 1236 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> power envelope, from 1.1 mW (only AON cluster), up to 1.36 W (whole SoC at maximum throughput); 2) 5.5–28.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> energy efficiency gain from offloading compute kernels from A53 to eFPGA; 3) 2.94 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> latency improvement using coherent memory access (CCA cluster); and 4) 55 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> MobileNetV1 energy per inference improvement on CCA compared to the CPU baseline. The overall flexibility-efficiency range on SMIV spans measured energy efficiencies of 1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> (dual-core A53), 3.1 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> (A53 with SIMD), 16.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> (eFPGA), 54.9 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> (CCA), and 256 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> (AON) at a peak efficiency of 4.8 TOPS/W.
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio et al. · Proceedings of the IEEE · 1998 · 56.5K citations · Full text
Engineering, Machine Learning, Multilayer Neural Networks +17
Transition-Aware Human Activity Recognition Using Smartphones
Jorge-L. Reyes-Ortiz, Luca Oneto, Albert Samà et al. · Neurocomputing · 2015 · 716 citations · Full text