AI Solutions

Biscotti

The Biscotti E1.S AI Module uses up to two Hailo-8 Edge AI processors, delivering up to 26 tera-operations per second (TOPS) each for a total of 52 TOPS while consuming as little as 10 watts. Built on an architecture optimized for neural networks, the Hailo-8 processors enable edge devices to run deep learning applications efficiently, effectively, and sustainably. Designed in the E1.S form factor, the module can be inserted directly into standard E1.S SSD slots to instantly add AI acceleration to larger server systems. This enables scalable deployments ranging from parallel neural networks processing large camera arrays to combined configurations supporting large AI workloads such as LLM inference. Compared to GPU modules or traditional add-in cards, the Biscotti E1.S delivers high AI performance with significantly lower power consumption, helping improve data center power efficiency. The module also includes a robust software suite with out-of-the-box support for state-of-the-art deep learning models, along with a comprehensive dataflow compiler that allows developers to quickly and easily port their neural network models.

 

Features

  • E1.S AI Module
  • AI Accelerator: Hailo-8
  • Up to 1600 TOPs of inference performance with air-cooled Dual CPU Servers
  • Inference for Visual Analysis or Generative AI
  • Plug-and-play and hot swappable
  • Easily upgrade from a Biscotti E1.S AI module to the next generation, doubling performance instantly
  • Use 20% of the Watts with TPUs compared to training GPUs
  • 20% of ASP and TCO compared to training GPU servers
  • 12-week lead times, significantly less than the typical for GPU servers
  • Generative AI models with up to 1.8 billion parameters

Benefits

Performance vs Power

The Biscotti E1.S AI Module provides 52 TOPS from as little as 10 Watts. The Biscotti E1.S uses 2 Hailo-8 Edge AI processors, featuring up to 26 tera-operations per second (TOPS) each, stands out for its exceptional performance in the realm of edge processor modules. By using an E1.S, it becomes feasible to power both AI processors, resulting in performance that excels in power efficiency.

Plug-and-Play for Servers and Edge Devices

The Biscotti E1.S can be inserted directly into E1.S slots normally used by SSDs to instantly provide Artificial Intelligence in a much larger server configuration. Either for use to support multiple parallel Neural Networks from a large array of camera inputs or all integrated together as a single Large Language Model array to solve the largest AI cases. At much lower power than GPU modules or Add-In-Cards, a solution using Biscotti can change the game for a data center’s power envelope.

Neural Network Models & Application Support

The AI processors integrated onto the Biscotti E1.S have a robust software suite that supports state-of-the-art deep learning models & applications out-of-the-box. Additionally, it is equipped with a comprehensive dataflow compiler that enables customers to port their neural network models easily & quickly. With support for AI frameworks: TensorFlow, TensorFlow Lite, Keras, PyTorch & ONNX, Biscotti supports Edge Neural Nets today and LLM Generative AI in the future.

Specifications

Biscotti AI Module
AI ProcessorDual Hailo-8
Form FactorE1.S (EDSFF)
Power (max)10 Watts
Performance52 TOPS
Interface4 Lane PCIe Gen 3
Supported FrameworksTensorFLow, TensorFlow Lite, ONNX, Keras, Pytorch
Supported OSLinux, windows
CertificationCE, FCC, VCCi, KCC, WEEE
Dimension33.75mm x 118.75mm x 9.5mm (5.9mm w/o Heat Sink Enclosure)
Operating Temperature0°C ~ 70°C
Biscotti Performance (With Atom X6414RE)
Network (AI)Frames per Second (HW)
Resnet_V1_50-Imagenet2692.61
Yolov3_Gluon50
Yolov350.4
Yolov4-leaky56.59
Centerpose_regentx_1.6gf4633.13
Eight Biscotti Module Performance (w/AMD Genoa System)
Network (AI)Frames per Second (HW)
Resnet_V1_50-Imagenet21.5K
Yolov3_Gluon400
Yolov3403.2
Yolov4-leaky452.72
Centerpose_regentx_1.6gf37.1K

Ordering Information

Part NumberNote
UCT201-021-NC Dual AI TPU w/12 lane Switch
UCT201-021-HS Dual AI TPU (9.5mm Heatsink) w/12 lane Switch
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