Source: Tiger Says Chip
Original Author: Tiger Says Chip
This article introduces the functional architecture, classification applications, challenges, and trends of DSP chips.
Comparing DSPs to “math prodigies”: while ordinary CPUs (like MCUs) excel at multitasking scheduling (the “liberal arts students”), DSPs specialize in quickly solving complex formulas (the “science students”), achieving “mental calculation capabilities” through hardware acceleration (like MAC units), making them suitable for the “high-intensity math competitions” of real-time signal processing.
1. Core Functions and Architectural Features
DSP (Digital Signal Processing) chips are microprocessors designed specifically for high-speed mathematical operations, with the primary goal of real-time processing of digital signals (such as audio, video, communication signals, etc.). Their architectural design focuses on efficiency optimization:
Harvard Architecture: Separate storage spaces for programs and data (dual buses) allow simultaneous reading of instructions and operating data, significantly improving throughput.
Pipelining Technology: Instruction execution is decomposed into multiple stages of parallel processing, such as fetching, decoding, and executing, similar to a factory assembly line, allowing multiple instructions to be completed in a single cycle.
Hardware Multiplier-Accumulator (MAC): Dedicated circuits achieve “multiply-accumulate” operations in a single cycle, suitable for core algorithms like filtering and FFT.
Multi-Address Generators and Parallelism: Support for multiple data streams for parallel access reduces memory access bottlenecks and enhances algorithm efficiency.
2. Classification and Technological Evolution
Data Formats:
Fixed-Point DSP: Low cost and power consumption, suitable for embedded systems (e.g., ADI Blackfin series).
Floating-Point DSP: High precision and large dynamic range, suitable for radar and medical imaging (e.g., ADI SHARC series).
Application Orientation:
General-Purpose: Highly programmable, adaptable to multiple scenarios (e.g., TI C6000 series).
Specialized: Optimized for specific algorithms (e.g., FFT acceleration modules for communication basebands).
Integration Trend: Modern DSPs are often integrated with MCUs, FPGAs, and other heterogeneous components to form SoCs (e.g., Xilinx Zynq), balancing control and computational power.
3. Application Scenarios and Design Considerations
Typical Applications:
Communication Systems: Beamforming and channel coding for 5G basebands (requiring high-throughput MAC units).
Automotive Electronics: New energy motor control (e.g., the Jinchip Electronics AVP32F335 chip achieving real-time torque calculations).
Consumer Electronics: Noise reduction and image processing in smartphones (relying on low-power fixed-point DSPs).
Key Selection Parameters:
Computational Speed: Measured in MMAC/s (million multiply-accumulates per second), needs to match algorithm complexity.
Memory and Peripherals: On-chip RAM capacity and the number of DMA channels affect real-time performance (e.g., multi-channel ADCs require high-speed data transfer).
Power Consumption and Heat Dissipation: Automotive DSPs must pass AEC-Q100 certification to ensure stability in high-temperature environments.
4. Design Challenges and Solutions
Thermal Management: 3D packaging (e.g., silicon through-silicon vias, TSV) enhances heat dissipation efficiency, preventing performance throttling.
Signal Integrity: Redistribution layer (RDL) optimization reduces parasitic effects, ensuring high-speed signal transmission.
Testing and Validation: Daisy Chain testing method for real-time monitoring of packaging reliability, locating failure points.
5. Future Trends
Heterogeneous Computing: Integration of DSPs with AI accelerators (e.g., NPUs) to support edge machine learning.
Advanced Processes: Processes below 12nm improve energy efficiency to meet ultra-low power demands of IoT.
Domestic Substitution: Domestic manufacturers are breaking through technical barriers, gradually replacing imports.
END
Reprinted content only represents the author’s views
Does not represent the position of the Institute of Semiconductors, Chinese Academy of Sciences
Editor: Silence
Responsible Editor: Muxin
Submission Email: [email protected]
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