کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4971311 | 1450467 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A 5.3 pJ/op approximate TTA VLIW tailored for machine learning
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
سخت افزارها و معماری
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A 5.3 pJ/op approximate TTA VLIW tailored for machine learning A 5.3 pJ/op approximate TTA VLIW tailored for machine learning](/preview/png/4971311.png)
چکیده انگلیسی
To achieve energy efficiency in the Internet-of-Things (IoT), more intelligence is required in the wireless IoT nodes. Otherwise, the energy required by the wireless communication of raw sensor data will prohibit battery lifetime, the backbone of IoT. One option to achive this intelligence is to implement a variety of machine learning algorithms on the IoT sensor instead of the cloud. Shown here is sub-milliwatt machine learning accelerator operating at the Ultra-Low Voltage Minimum-Energy Point. The accelerator is a Transport Triggered Architecture (TTA) Application-Specific Instruction-Set Processor (ASIP) targeted for running various Machine Learning algorithms. The ASIP is implemented in 28Â nm FDSOI (Fully Depleted Silicon On Insulator) CMOS process, with an operating voltage of 0.35Â V, and is capable of 5.3pJ/cycle and 1.8nJ/iteration when performing conventional machine learning algorithms. The ASIP also includes hardware and compiler support for approximate computing. With the machine learning algorithms, computing approximately brings a maximum of 4.7% energy savings.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Microelectronics Journal - Volume 61, March 2017, Pages 106-113
Journal: Microelectronics Journal - Volume 61, March 2017, Pages 106-113
نویسندگان
Jukka Teittinen, Markus Hiienkari, IndrÄ Å½liobaitÄ, Jaakko Hollmen, Heikki Berg, Juha Heiskala, Timo Viitanen, Jesse Simonsson, Lauri Koskinen,