Desirable Features of DSP Processors:

These features make DSP processors more suitable for signal processing tasks compared to
general-purpose processors:
 Harvard Architecture: Separate instruction and data memory.
 MAC Unit (Multiply-Accumulate): For efficient computation in filters, FFTs, etc.
 Parallelism & Pipelining: Improved speed by overlapping instruction execution.
 Zero-overhead looping: Efficient looping without extra clock cycles.
 Specialized addressing modes: Circular addressing for filters, bit-reversed addressing
for FFTs.
 High-speed I/O and DMA (Direct Memory Access): Faster data handling.
 Real-time processing capability.

Types of Architectures of DSP Processors:

 Von Neumann Architecture: Shared memory for data and instructions. Simpler, but
slower due to bottlenecks.
 Harvard Architecture: Separate memories and buses for data and instructions.
 Super Harvard Architecture (SHARC): Enhanced Harvard with features like SIMD
and high-speed DMA.
 VLIW (Very Long Instruction Word) : Executes multiple instructions in parallel.
 Multi-core DSPs: For more intensive real-time processing.

Architecture Description Pros Cons

Von Shared program and data
Simple design Memory bottleneck
Neumann memory
Harvard Separate memory and buses Faster execution More complex
Super Harvard + Instruction cache +
Efficient & fast Costly
Harvard DMA
Executes multiple instructions Needs compiler
VLIW High throughput
per clock support
SIMD & Multiple data processed High performance for Complexity in
Multi-core simultaneously parallel tasks software
Harvard Architecture in DSP:

Working Principle:
 Separate memory spaces: One for instructions (program code), one for data.
 Two buses: Instruction bus fetches instructions, and data bus handles data simultaneously.
 Parallelism: Since instruction and data accesses occur in parallel, CPU can execute instructions
without waiting for data fetch/store.
Advantages:
Faster Execution:
 Parallel access to instruction and data memory allows faster throughput.
Efficient Pipelining:
 Enables overlapping of instruction fetch and execution.
Reduces Bottlenecks:
 No competition between instructions and data for the same bus.
Ideal for DSP:
 Improves real-time signal processing performance where constant data fetching and instruction
decoding are required.
Disadvantages:
Increased Complexity:
 Requires more hardware (separate memory and buses).
Costlier Design:
 More memory and control circuitry → higher cost.
 Reduced Flexibility:
 Memory is partitioned; cannot dynamically allocate space between instruction and data.
Use in DSP Processors:
 Widely used in processors like TI TMS320, Analog Devices SHARC, etc.
 Enables real-time performance in audio, speech, and image processing.
Von Neumann Architecture DSP:

2. Working Principle:

 Single memory unit holds both instructions and data.

 A single system bus is used for both instruction fetch and data access.
 Operations are sequential—CPU must fetch an instruction, decode it, and then fetch/execute
data using the same bus, one at a time.
3. Advantages:
Simplicity:
 Unified memory space makes design simpler.
Cost-Effective:
 Requires fewer hardware resources—ideal for simpler embedded systems.
Easier Programming:
 Single address space simplifies compiler design and code generation.

4. Disadvantages:
Von Neumann Bottleneck:
 Shared bus leads to delays—CPU can't fetch instructions and data at the same time.
Slower Execution:
 Compared to Harvard architecture, execution is slower due to serialized operations.
Not Ideal for Real-Time DSP:
 Limited throughput hampers performance in high-speed applications.

Use in DSP (rarely):

 Used in low-power, cost-sensitive applications.

 Generally avoided in high-performance DSP due to memory access limitations.
3. Introduction to Multi-rate Signal Processing
Definition: “Multi-rate signal processing involves techniques where signals are processed at
multiple sampling rates within a single system.”
This is done by:
 Reducing the sampling rate: Decimation
 Increasing the sampling rate: Interpolation
 These techniques help in efficient computation, memory savings, and matching different
system sampling rates.
 Key Concepts:
o Decimation (Down-sampling): Reducing the sampling rate.
o Interpolation (Up-sampling): Increasing the sampling rate.
o Applications: Efficient filter implementation, sample rate conversion in
audio/video processing.
Applications of Multi-rate Signal Processing
 Audio Sample Rate Conversion: Between CD, MP3, and broadcast standards.
 Sub-band Coding: In image and audio compression (e.g., MP3, JPEG 2000).
 Efficient Filter Design: Lower rates → smaller filter size.
 Software-Defined Radio (SDR): Different channel bandwidths and sampling rates.

4. Adaptive Filtering
An adaptive filter is a system that automatically adjusts its filter coefficients to adapt to changes in the
input signal or environment. Unlike fixed filters (like FIR or IIR), adaptive filters update themselves to
minimize an error signal based on some optimization criterion (usually the Mean Square Error).
Basic Working Principle:
1. Input signal → processed by filter with adjustable coefficients.
2. Filter output is compared to a desired signal.
3. Error signal is computed:
e(n)=d(n)−y(n)e(n) = d(n) - y(n)e(n)=d(n)−y(n)
4. Based on this error, the filter adapts its coefficients using a specific algorithm (e.g., LMS or RLS).
Applications of Adaptive Filtering:
1. Noise Cancellation: Removes unwanted noise from signals.
o E.g., Active noise-canceling headphones.
2. Echo Cancellation: Used in telephony and VoIP.
o Removes echo from speakerphone.
3. Channel Equalization: In digital communication to correct channel distortions.
4. Biomedical Signal Processing:
o Remove artifacts from ECG, EEG signals.
5. System Identification:
o Modeling unknown systems by matching outputs.
6. Speech Enhancement:
o Improve clarity in noisy environments.
5. Case Study
Usually involves:

 Implementation of a real-world application using a DSP processor.

 Example Topics:
o Audio signal enhancement
o ECG signal analysis
o Image processing using DSP kits (like TI C6000 series)
o Real-time FFT on DSP

6. Advanced Topics (Based on Course)

Could include:

 DSP in AI & ML: Edge computing, audio event detection, etc.

 Embedded DSP Systems
 Real-Time Operating Systems (RTOS) with DSPs
 Power optimization in DSP designs
 Custom hardware (FPGA) for DSP

Applications:

 Noise cancellation (e.g., in headphones)

 Echo cancellation (e.g., in VoIP)
 Channel equalization
 Financial forecasting

5. Case Study: Real-time DSP Application

Example: Real-Time Audio Equalizer using TI C6000 DSP

Components:

 DSP Kit (e.g., TMS320C6748)

 Microphone input → ADC
 Digital filters (FIR/IIR) implemented in C/assembly
 Output through DAC to speakers

Steps:

1. Read analog audio input via ADC.

2. Apply band-pass filters for equalization.
3. Output processed signal in real-time.
Outcome: Demonstrates DSP’s low-latency performance and efficiency in audio processing.

6. Advanced Topics

 Real-Time Operating Systems (RTOS): Integrating DSP in systems with real-time

constraints.
 DSP + Machine Learning:
o Voice recognition on embedded DSPs.
o Audio scene classification.
 Embedded DSP Systems: Use in hearing aids, smart devices, and medical instruments.
 Low Power DSP Design: Crucial for battery-powered IoT devices.
 FPGAs for DSP: Hardware-based acceleration for intensive tasks (e.g., vision
processing).

Unit V: DSP Processor & Applications – Important

Questions

1. Desirable Features of DSP Processors

1. List and explain the desirable features of a typical DSP processor.

2. Why is the MAC (Multiply-Accumulate) unit important in DSP applications?
3. Explain how zero-overhead looping benefits DSP applications.
4. Differentiate between standard microprocessors and DSP processors.

2. Types of Architectures of DSP Processors

1. Compare Von Neumann and Harvard architectures with respect to DSP

applications.
2. What is Super Harvard architecture? Discuss its advantages.
3. Explain the concept of VLIW architecture in DSP processors.
4. Describe how SIMD and multi-core architectures enhance DSP performance.

3. Multi-rate Signal Processing

1. Define multi-rate signal processing and explain its significance.

 2. Differentiate between decimation and interpolation with suitable diagrams.
3. Explain the need for anti-aliasing filters in multi-rate systems.
4. Discuss applications of multi-rate signal processing in real-world systems.

4. Adaptive Filtering

1. What is adaptive filtering? Explain the concept with a basic diagram.

2. Describe the LMS algorithm and its application.
3. Compare LMS and RLS algorithms in terms of convergence and complexity.
4. List any three applications of adaptive filters and explain one in detail.

5. Case Study (Example-Based Questions)

1. Design a real-time audio filter using a DSP processor and explain its operation.
2. Explain the steps involved in implementing a real-time FFT using a DSP kit.
3. Discuss a case study of DSP application in biomedical signal processing.
4. Describe the DSP processor selection criteria for real-time speech processing.

6. Advanced Topics

1. Discuss the role of DSP processors in embedded systems and IoT.

2. How is real-time operating system (RTOS) integrated with DSP applications?
3. Explain the application of DSP in AI/ML-based systems.
4. What are the power optimization techniques used in modern DSP systems?
5. Compare FPGA and DSP processors in terms of performance for signal processing
tasks.