Mapping Signal Processing Algorithms to Hardware Architectures

Bridging the gap between mathematical signal processing algorithms and physical hardware is essential for building fast, energy-efficient devices. This course guides you through the systematic process of mapping complex DSP algorithms onto real-world silicon architectures. You will transition from writing abstract mathematical formulas to designing high-performance hardware structures. By understanding how data flows through processors, FPGAs, and custom silicon, you will learn to optimize your designs for speed, power, and area. What you'll learn: Understand foundational DSP algorithms, hardware limitations, and architectural trade-offs; Apply pipelining and parallel processing techniques to maximize system throughput; Implement folding and unfolding transformations to optimize silicon area and execution speed; Design systolic arrays for efficient matrix multiplication and modern neural network acceleration; Analyze power-reduction strategies and energy-efficient computing paradigms for edge devices; Practice mapping algorithms to physical architectures using structured written exercises. The course starts with fundamental concepts of signal processing and hardware architectures before moving into advanced transformation techniques, data-flow graphs, and modern optimization strategies for edge hardware. This course is designed for beginners, students, and engineers looking to understand hardware-software co-design with no prior advanced hardware description language experience required. Start reading today to master the art of hardware-efficient algorithm design.