A 5.3 pJ/op approximate TTA VLIW tailored for machine learning

作者：Teittinen Jukka^*; Hiienkari Markus; Zliobaite Indre; Hollmen Jaakko; Berg Heikki; Heiskala Juha; Viitanen Timo; Simonsson Jesse; Koskinen Lauri

来源：Microelectronics Journal, 2017, 61: 106-113.

DOI：10.1016/j.mejo.2017.01.007

• Integrated circuit
• Processor
• Approximate computing
• Minimum energy point
• Machine learning
• Timing error detection

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 28 arts FDSOI (Fully Depleted Silicon On Insulator) CMOS process, with an operating voltage of 0.35 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.

• 出版日期2017-3