LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

High-Throughput Low Power Area Efficient 17-bit 2’s Complement Multilayer Perceptron Components and Architecture for on-Chip Machine Learning in Implantable Devices

Photo by acfb5071 from unsplash

In this manuscript the authors, design new hardware efficient combinational building blocks for a Multi Layer Perceptron (MLP) unit which eliminates the need for hardware generic Digital Signal Processing (DSP)… Click to show full abstract

In this manuscript the authors, design new hardware efficient combinational building blocks for a Multi Layer Perceptron (MLP) unit which eliminates the need for hardware generic Digital Signal Processing (DSP) units and also eliminates the need for on-chip block RAMs (BRAMs). The components were designed to minimise power and area consumption without sacrificing throughput. All designs were validated in a Field Programmable Gate Array (FPGA) and compared against unrestricted CPU-MATLAB implementations. Furthermore, a (2,2,2,2) MLP with back propagation was implemented and tested in a FPGA showing a total hardware utilisation of just 3782 LUTs, and no DSP or BRAMs. The MLP was also built in a Application Specific Integrated Circuit (ASIC) using a 130 nm technology by Skywater 130A. The results show that the area occupation was just $0.12~mm^{2}$ and consumed just 100 mW at 100 MHz input stimulus.

Keywords: perceptron; area; power area; high throughput; chip

Journal Title: IEEE Access
Year Published: 2022

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.