Abstract: Disclosed herein are systems and methods for quantizing floating-point values during operation of robotic systems. For example, a system can be configured to receive input data having multiple values represented in a floating-point format and determine a maximum absolute value from these values. The system can adjust the representation of each value by removing at least one bit from the exponent portion and redistributing it to the mantissa portion. In examples, the system can also extract a sign bit and align each value by shifting each bit. Based on the adjusted values, the system can perform dynamic quantization of the input data based at least in part on a scaling value determined using the maximum absolute value. The input data can then be provided as an input to at least a portion of a neural network during execution of one or more operations by the neural network.

Abstract: Embodiments include systems and methods for processing sensor data and generating operational instructions for automated devices. The automated device includes hardware (e.g., INT8 MAC units, FP32 SIMD processors) and software operations executing a machine-learning architecture having transformer architectures implementing mixed-precision quantization for ingesting and analyzing various types of sensor data. The machine-learning architecture implements a transformer that includes dynamic quantization layers that dynamically quantize tensors, per-token, for downstream operations of the transformer and machine-learning architecture.

Abstract: Embodiments include systems and methods for processing sensor data and generating operational instructions for automated devices. The automated device includes hardware (e.g., INT8 MAC units, FP32 SIMD processors) and software operations executing a machine-learning architecture having transformer architectures implementing mixed-precision quantization for ingesting and analyzing various types of sensor data. The machine-learning architecture implements a transformer that includes dynamic quantization layers that dynamically quantize tensors, per-token, for downstream operations of the transformer and machine-learning architecture.