fasiformer

Fasiformer is a family of transformer-based neural networks designed to deliver fast inference on resource-constrained hardware while preserving accuracy. The aim is to enable real-time or near real-time processing on devices such as smartphones, embedded processors, and edge servers. Fasiformer architectures typically incorporate efficient attention mechanisms and memory-conscious training techniques.

Core design principles include substituting standard quadratic self-attention with linear-time or kernel-based approximations, such as kernelized attention or structured sparse patterns. Reversible or memory-efficient residual blocks reduce peak memory during training. Additional techniques often used in Fasiformer deployments include quantization, mixed-precision computation, weight sharing, and task-specific adapters to improve efficiency without large accuracy losses. Models may also employ compact positional encodings and layer-wise normalization to further reduce computation.

In practice, Fasiformer architectures come in encoder, decoder, or encoder-decoder variants. They use the usual transformer building blocks — multi-head self-attention, feed-forward networks, residual connections, and layer normalization — but arranged with efficiency-focused attention modules. They are suitable for autoregressive, bidirectional, and multimodal settings.

Applications include natural language processing tasks such as translation and summarization, real-time transcription, voice assistants, and on-device inference for mobile and IoT, as well as multimodal tasks combining text, audio, and visuals. Evaluations report improvements in latency and memory footprint relative to standard transformers, with task- and hardware-dependent trade-offs in accuracy. These trade-offs vary by model size, task, and hardware.

Related topics include efficient transformers, Linformer, Reformer, and Longformer.