The embARC Machine Learning Inference (MLI) library provides software functions optimized for DSP-enhanced ARC EMxD and ARC HS4xD processors. It enables ARC customers to efficiently develop or port data processing algorithms based on machine learning (ML) principles. Supported ARC processors include:
- EM5D, EM7D, EM9D, and EM11D
- HS45D and HS47D
CIFAR-10 CNN Model on ARC EM Processor
MLI addresses a broad range of NN applications
Primarily addressing IoT related applications, the embARC MLI library extends MIPS' artificial intelligence offering to multiple IoT use cases:
| Application | Example NN use cases |
|---|---|
| Voice-based human machine interfaces |
|
| Personal fitness and health monitoring |
|
| Industrial IoT |
|
MLI kernels support multiple machine learning models
The embARC MLI software library provides a set of essential kernels for effective inference of small or mid-sized ML models. It enables the efficient implementation of convolutional neural networks (CNNs) [ex. classic and depth-wise convolutions], recurrent neural networks (RNNs) [ex. long short-term memory (LSTM) cells and basic RNN cells], fully connected layers, poolings, activation functions [ex. rectified linear units (ReLU)], and data routing operations [ex. padding, transposing, and concatenation], while reducing the power and memory footprint.
Leveraging the right processors for machine learning
ML-based applications intensively use classic DSP, RISC, and matrix operations, each with unique processing needs. ARC EM DSP and ARC HS DSP processors offer the best combination of power and area on the ML spectrum.
Availability
The embARC MLI software library is available through embARC.org, a dedicated website that provides software developers centralized access to free and open source software, drivers, operating systems, and middleware supporting ARC processors. The embARC MLI distribution is managed by MIPS for the community and all contributions are welcome.
Highlights & Key Features
- Optimized for low-power IoT applications that utilize convolutional neural networks (CNNs) and recurrent neural networks (RNNs)
- Supports the energy-efficient, DSP-enhanced ARC EMxD and HS4xD Processors
- Boosts performance up to 16x for 2D convolution layers compared to unoptimized implementations
- Acceleration of RNNs up to 5x for a wide range of topologies including those built with long short-term memory (LSTM) cells
- Distributed as free and open-source software through the embARC.org website
Resources
Brochure MIPS IP Brochure A comprehensive portfolio of leading-edge MIPS IP. Download
Article MIPS IP Technical Bulletin In-depth technical articles, white papers, videos, webinars, product announcements and more. Read now
Brochure IP Solutions for Edge AI A collection of articles on innovations and best practices. Download
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