Transformers Blog Posts

Audio-Native Transformers: Understanding Models That Process Raw Sound Instead of Text-to-Speech

For many years, AI's interaction with the human voice followed a rigid, multi-stage pipeline: raw audio is first converted into text via Speech-to-Text (STT), then processed by a Large Language Model (LLM), and finally, a text response is synthesized into audio via Text-to-Speech (TTS). While functional, this traditional approach suffers from several critical limitations:

Beyond Transformers: Sparse Architectures and State-Space Models for Infinite Context

The Transformer architecture, and its self-attention mechanism, ignited the modern AI revolution. Its ability to capture complex relationships between tokens in a sequence is unparalleled. However, this power comes at a steep architectural cost. The computational complexity and memory usage of self-attention grow quadratically (O(n²)) with the length of the input sequence.

FlashAttention-3 and Beyond: How Hardware-Aware Algorithms are Making Models 10x Faster

The Transformer architecture, with its powerful self-attention mechanism, revolutionized AI. However, as discussed in previous articles, its quadratic (O(N²)) complexity with respect to sequence length (N) presents a significant challenge for handling very long contexts. But beyond raw FLOPs (floating-point operations), there lies an even more insidious bottleneck for Transformer performance on modern GPUs: memory bandwidth.

Linear Transformers: Can We Finally Achieve O(n) Complexity for Infinite Context?

The Transformer architecture, built upon the self-attention mechanism, has been the bedrock of modern AI, revolutionizing natural language processing and beyond. Its ability to capture complex dependencies across an entire input sequence is unparalleled. However, this power comes with a significant architectural cost: the computational and memory complexity of self-attention scales quadratically (O(N²)) with the sequence length (N).

Performance Analysis Mamba Vs Transformer

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The Transformer Breakdown: A Deep Dive into Self-Attention, Key-Value Pairs, and Positional Encoding

The Transformer architecture, introduced in the seminal 2017 paper "Attention Is All You Need," single-handedly revolutionized the field of Artificial Intelligence, particularly Natural Language Processing (NLP). Before Transformers, the dominant models—Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs)—processed information sequentially, one word at a time. This made them slow to train on large datasets and prone to "forgetting" context over long sentences.

The Vanishing Gradient Problem: How Transformers Solved What Killed Earlier RNNs

In the early days of deep learning for sequence data—tasks like natural language processing or time series analysis—Recurrent Neural Networks (RNNs) were the undisputed champions. Their ability to process information sequentially, maintaining an internal "memory" from one step to the next, seemed perfectly suited for understanding context over time. However, RNNs harbored a critical, hidden flaw that severely limited their potential: the Vanishing Gradient Problem.