Transformer
/trænsˈfɔːrmər/
noun … “a neural network architecture that models relationships using attention mechanisms.”
Convolutional Neural Network
/ˌsiːˌɛnˈɛn/
noun … “a deep learning model for processing grid-like data such as images.”
American Institute of Electrical Engineers
/ˌeɪ.iːˌiːˈiː/
noun … “the original American institute for electrical engineering standards and research.”
IEEE
/ˌaɪ.iːˌiːˈiː/
noun … “the global standards organization for electrical and computing technologies.”
Float64
/floʊt ˈsɪksˌtiːfɔːr/
noun … “a 64-bit double-precision floating-point number.”
Float32
/floʊt ˈθɜːrtiːtuː/
noun … “a 32-bit single-precision floating-point number.”
INT8
/ɪnˈteɪt/
n. “small numbers, absolute certainty.”
INT8 is an 8-bit two's complement integer ranging from -128 to +127, optimized for quantized neural network inference where model weights/activations rounded to nearest integer maintain >99% accuracy versus FP32 training. Post-training quantization or quantization-aware training converts FP32 networks to INT8, enabling 4x throughput and 4x memory reduction on edge TPUs while zero-point offsets handle asymmetric activation ranges.
Key characteristics of INT8 include:
Floating Point 16
/ˌɛf ˈpiː ˈsɪks ˈti:n/
n. "IEEE 754 half-precision 16-bit floating point format trading precision for 2x HBM throughput in AI training."
Floating Point 32
/ˌɛf ˈpiː ˈθɜr ti ˈtu/
n. "IEEE 754 single-precision 32-bit floating point format balancing range and accuracy for graphics/ML workloads."
RNN
/ɑr ɛn ˈɛn/
n. "Neural network with feedback loops maintaining hidden state across time steps for sequential data processing."