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Pairing Convolutional Neural Networks with Transformer Models

For over a decade, CNNs were the most popular deep-learning model for vision processing applications. As they evolved, CNNs accurately supported an expanded set of use cases, including image classification, object detection, semantic segmentation (grouping or labeling every pixel in an image), and panoptic segmentation (identifying object locations as well as grouping or labeling every pixel in every object).

Although the computer vision application landscape was understandably dominated by CNNs, a new algorithm category¡ªinitially developed for natural language processing (NLP) such as translation and queries¡ªhas made serious inroads beyond ChatGPT. Known as a transformer, the deep-learning model pioneered by Google beats CNNs in accuracy without any modifications other than replacing language patches with image patches. Moreover, Google¡¯s vision transformer (ViT), an optimized model based on the original transformer architecture, outperforms a comparable CNN with four times fewer computational resources.

This is because transformers use property attention mechanisms and sparsity to improve training, focus on relevant data, and bolster inference capabilities. With these techniques, transformers can better learn and understand more complex patterns¡ªas CNNs typically address data frames without knowing what came before or after. Nevertheless, it is important to note that while transformers deliver higher accuracy, CNNs achieve significantly higher frames per second (FPS). That¡¯s why transformers are frequently paired with CNNs¡ªto bolster the speed and accuracy of vision processing applications.

, introduced by Apple in early 2022, is an example of this approach. Essentially, MobileViT merges transformer and CNN features to create a lightweight model for vision classification. The combination of transformer and convolution, when compared to the CNN-only MobileNet, delivers a three percent higher accuracy rate for the same size model (6M coefficients).

Boosting Image and Video Inference Efficiency

Unsurprisingly, there are now many purpose-built transformers that support image and video inference with increased accuracy. , for example, achieves the best reported numbers on several challenging action recognition benchmarks, including the Kinetics-400 action recognition data set. Moreover, compared with modern 3D CNNs, TimeSformer is approximately three times faster to train and requires less than one-tenth the amount of compute resources for inference. The sheer scalability of TimeSformer enables the training of large models on extended video clips and opens the door for companies to design more intelligent edge AI systems.

Designed for surveillance video anomaly detection (SVAD),  is a hybrid transformer and CNN mechanism that leverages spatial and temporal information from surveillance videos to detect anomalous events. TransCNN detects anomalies with a high level of accuracy by combining an efficient backbone CNN model for spatial feature extraction with a transformer-based model that learns long-term temporal relationships between different complex surveillance events. TransCNN outperforms other state-of-the-art approaches, achieving high AUC values of 94.6%, 98.4%, and 89.6% on the ShanghaiTech, UCSD Ped2 and CUHK avenue datasets, respectively.

Optimizing AR/VR Applications

Vision transformer models are also helping system designers optimize AR and VR applications. For example, a research team at the University of Science and Technology of China recently combined a transformer-based encoder with a CNN-based decoder to improve conventional depth estimation. Dubbed , the hybrid technique efficiently recovers the depth value of each pixel from a single RGB image. When pitted against NYU Depth V2 (indoor) and KITTI (outdoor) benchmarks, MDE achieves top performance scores on both quantitative and qualitative evaluations.

Another team of researchers from Stanford University, NVIDIA, and the University of Hong Kong developed the transformer-based COPILOT to predict and avoid collisions for people immersed in AR and VR environments. To train COPILOT, the research team developed a synthetic data generation framework that generates videos of virtual humans moving and colliding within diverse 3D scenarios. Extensive testing demonstrates COPILOT successfully generalizes unseen synthetic as well as real-world scenes, with outputs that can be harnessed to further improve downstream collision avoidance.

Accelerating Both CNNs and Transformers

Although most current AI accelerators are optimized for CNNs, many of them are not ideal for transformers, as the latter demands considerable compute capabilities and memory resources to perform complex calculations and support sparsity. Optimized for both CNNs and transformers, the ARC  NPX6 NPU IP is an accelerator that efficiently enables high-performance edge AI use cases within a minimal power envelope by:

• Integrating hardware and software connectivity features for multiple NPU instances to achieve up to 3,500 tera operations per second (TOPS) of performance on a single SoC
• Delivering, in a single instance, up to 250 TOPS at 1.3 GHz on 5nm processes in worst-case conditions, or up to 440 TOPS by using sparsity, which increases performance while decreasing energy demands of executing a neural network
• Seamlessly scaling from 4K to 96K multiply¨Caccumulate operations (MACs)
• Offering optional 16-bit floating-point support inside the neural processing hardware to maximize layer performance and simplify the transition from GPUs used for AI prototyping to high-volume power- and area-optimized SoCs

The ARC NPX6 NPU IP is paired with the Synopsys ARC? MetaWare MX Development Toolkit which includes compilers and debugger, neural network software development kit (SDK), virtual platforms SDK, runtimes and libraries, and advanced simulation models. To help designers accelerate time to market, the toolkit automatically compiles and optimizes models for CNNs, transformers, recommenders, recurrent neural networks (RNNs), and long short-term memory networks (LSTMs).

ARC NPX6 NPU IP Earns ¡°Best Edge AI Processor¡± Award

Many companies leverage the ARC NPX6 NPU IP to design advanced edge AI systems for a wide range of applications. At the Embedded Vision Summit last night, the ARC NPX6 NPU IP was awarded ¡°Best Edge AI Processor¡± in the 2023 Edge AI and Vision Product of the Year Awards presented by the Edge AI and Vision Alliance. When announcing the award, Jeff Bier, founder of the , told attendees that Synopsys is a consistent, agile innovator as demonstrated by winning Edge AI and Vision Product of the Year Awards two years running ¨C 2022 Best Automotive Solution for Synopsys ARC EV7xFS Processor IP and the 2023 Best Edge AI Processor.

¡°I congratulate the Synopsys team on earning this distinction for the ARC NPX6 NPU IP, which continues Synopsys¡¯ strong tradition of innovation in processors for embedded AI and computer vision,¡± stated Jeff Bier.