Cadence Launches On-Device Tensilica AI Platform

Cadence Design Systems today unveiled its Tensilica AI Platform for accelerating artificial intelligence software on a chip development, including three supporting product families optimized for varying data and on-device AI requirements.

Spanning the low, mid and high end, the Cadence ensilica AI Platform delivers scalable and energy-efficient on-device to edge AI processing. A new companion AI neural network engine (NNE) consumes 80 percent less energy per inference.

Targeting intelligent sensor, internet of things (IoT) audio, mobile vision/voice AI, IoT vision and advanced driver assistance systems (ADAS) applications, the Tensilica AI Platform product families include the following:

  • AI Base, which Includes the Tensilica HiFi DSPs for audio/voice, Vision DSPs, and ConnX DSPs for radar/lidar and communications, combined with AI instruction-set architecture (ISA) extensions.
  • AI Boost:, which adds a companion NNE, initially the Tensilica NNE 110 AI engine, which scales from 64 to 256 GOPS and provides concurrent signal processing and efficient inferencing.
  • AI Max, which encompasses the Tensilica NNA 1xx AI accelerator family, which integrates the AI Base and AI Boost technology. The multicore NNA accelerators can scale up to 32 TOPS.

All of the NNE and NNA products include random sparse compute to improve performance, run-time tensor compression to decrease memory bandwidth, and pruning plus clustering to reduce model size.

Comprehensive common AI software addresses all target applications. This software includes the Tensilica Neural Network Compiler, which supports these industry-standard frameworks: TensorFlow, ONNX, PyTorch, Caffe2, TensorFlowLite, and MXNet for automated end-to-end code generation; Android Neural Network Compiler; TFLite Delegates for real-time execution; and TensorFlow Lite Micro for microcontroller-class devices.

"AI SoC developers are challenged to get to market faster with cost-effective, differentiated products offering longer battery life and scalable performance," said Sanjive Agarwala, corporate vice president and general manager of the IP Group at Cadence, in a statement. "With our mature, extensible. and configurable platform based on our best-in-class Tensilica DSPs and featuring common AI software, Cadence allows AI SoC developers to minimize development costs and meet tight market windows. By enabling AI across all performance and price points, Cadence is driving the rapid deployment of AI-enabled systems everywhere."

"Scaling low power on-device AI capabilities requires extremely efficient multi-sensory compute. Cadence and the TensorFlow Lite for Microcontrollers (TFLM) team have been working together for many years to co-develop solutions that enable the most cutting-edge, low-footprint use cases in the AI space. The trend for real-time audio networks to use LSTM-based neural nets for the best performance and efficiency is a key example. Working closely with Cadence, we are integrating a highly optimized LSTM operator on Tensilica HiFi DSPs that enables the next level of performance improvements for key use cases like voice-call noise suppression. We are excited to continue this collaboration and provide industry-level innovation in the low-energy AI space," said Pete Warden, technical lead of TensorFlow Lite Micro at Google, in a statement.

"Integrating a Cadence Tensilica HiFi 4 DSP into the NXP i.MX RT600 crossover MCU not only provides high-performance DSP capabilities for a broad range of audio and voice processing applications, but also increases inference performance, enabling AI even in very low-power, battery-operated products. The HiFi neural network library allows NXP to take full advantage of the AI capabilities of the HiFi 4 DSP and integrate it into NXP's eIQ Machine Learning Software Development Environment supporting the TensorFlow Lite Micro and Glow ML inference engines," said Cristiano Castello, senior director of microcontrollers product innovation at NXP Semiconductors, in a statement.

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