M1076 Analog Matrix Processor
Overview
The M1076 Mythic AMP™ delivers up to 25 TOPS in a single chip for high-end edge AI applications. The M1076 integrates 76 AMP tiles to store up to 80M weight parameters and execute matrix multiplication operations without any external memory. This allows the M1076 to deliver the AI compute performance of a desktop GPU while consuming up to 1/10th the power - all in a single chip.
Features
Array of 76 AMP tiles, each with a Mythic Analog Compute Engine (Mythic ACE™)
On-chip DNN model execution and weight parameter storage with no external DRAM
Execution of models at higher resolution and lower latency for better results
4-lane PCIe 2.1 interface with up to 2GB/s of bandwidth for inferencing processing
19mm x 15.5mm BGA package
Capacity for up to 80M weights - able to run single or multiple complex DNNs entirely on-chip
Deterministic execution of AI models for predictable performance and power
Support for INT4, INT8, and INT16 operations
Available I/Os – 10 GPIOs, QSPI, I2C, and UARTs
Typical power consumption running complex models 3~4W
Workflow
DNN models developed in standard frameworks such as Pytorch, Caffe, and TensorFlow are implemented and deployed on the Mythic Analog Matrix Processor (Mythic AMPTM) using Mythic’s AI software workflow. Models are optimized, quantized from FP32 to INT8, and then retrained for the Mythic Analog Compute Engine (Mythic ACETM) prior to being processed through Mythic’s powerful graph compiler. Resultant binaries and model weights are then programmed into the Mythic AMP for inference. Pre-qualified models are also available for developers to quickly evaluate the Mythic AMP solution.
DNN Model Library
Mythic provides a library of pre-qualified DNN models for the most popular AI use cases. The DNN models have been optimized to take advantage of the high-performance and low-power capabilities of the Mythic Analog Matrix Processor (Mythic AMPTM). Developers can focus on model performance and end-application integration instead of the time-consuming model development and training process. Available pre-qualified DNN models include:
