Inhaltszusammenfassung zur Seite Nr. 1
Vodafone Chair Mobile Communications Systems, Prof. Dr.-Ing. G. Fettweis chair
Digital Signal Transmission Lab
SS 08
Oliver Arnold
Steffen Kunze
Inhaltszusammenfassung zur Seite Nr. 2
Introduction chair Hardware Why to use digital signal processing? General introduction to DSPs The TMS320C6711 DSP Architecture Overview Peripherals DSK6711 evaluation board - Software Code Composer Studio DSP/BIOS Multi-channel Buffered Serial Port (McBSP) TU Dresden, 4/29/2008 Slide 2
Inhaltszusammenfassung zur Seite Nr. 3
chair Hardware TU Dresden, 4/29/2008 Slide 3
Inhaltszusammenfassung zur Seite Nr. 4
Digital Signal Processing (DSP) chair Wireless / Cellular HDD Voice-band audio PRML read channel RF codecs MR pre-amp Voltage regulation Servo control SCSI tranceivers Consumer Audio DSP: Automotive Stereo A/D, D/A PLL Digital radio A/D/A Technology Mixers Active suspension Enabler Voltage regulation Multimedia Stereo audio DTAD Imaging Speech synthesizer Graphics palette Mixed-signal Voltage regulation processor TU Dresden, 4/29/2008 Slide 4
Inhaltszusammenfassung zur Seite Nr. 5
System Considerations chair Performance Performance Interfacing Interfacing Power Power Size Size Ease-of Use Integration Ease-of Use Integration Cost Cost • Programming • Memory • Programming • Memory • Device cost • Device cost • • Interfacing Interfacing • • Peripherals Peripherals • System cost • System cost • Debugging • Debugging • Development cost • Development cost • Time to market • Time to market TU Dresden, 4/29/2008 Slide 5
Inhaltszusammenfassung zur Seite Nr. 6
Why Go Digital? chair Digital signal processing techniques are now so powerful that sometimes it is extremely difficult, if not impossible, for analogue signal processing to achieve similar performance. Examples: FIR filter with linear phase Adaptive filters TU Dresden, 4/29/2008 Slide 6
Inhaltszusammenfassung zur Seite Nr. 7
Why Go Digital? chair Analogue signal processing is achieved by using analogue components such as: Resistors Capacitors Inductors The inherent tolerances associated with these components, temperature, voltage changes and mechanical vibrations can dramatically affect the effectiveness of the analogue circuitry TU Dresden, 4/29/2008 Slide 7
Inhaltszusammenfassung zur Seite Nr. 8
Why Go Digital? chair With DSP? - It is easy to: Change applications Correct applications Update applications Additionally DSPs reduce: Noise susceptibility Chip count Development time Cost Power consumption TU Dresden, 4/29/2008 Slide 8
Inhaltszusammenfassung zur Seite Nr. 9
chair General Introduction to DSPs TU Dresden, 4/29/2008 Slide 9
Inhaltszusammenfassung zur Seite Nr. 10
What Problem Are We Trying To Solve? chair xY ADC DAC DSP Digital sampling of Most DSP algorithms can be an analog signal: expressed as: count A Y = Σ a * x i i i = 1 for (i = 1; i < count; i++){ t sum += m[i] * n[i]; } TU Dresden, 4/29/2008 Slide 10
Inhaltszusammenfassung zur Seite Nr. 11
What are the typical DSP algorithms? chair The Sum of Products (SOP) is the key element in most DSP algorithms: Algorithm Equation M yn () = a x(n −k) ∑ k Finite Impulse Response Filter k =0 M N y(n) = a x(n −k) + b y(n −k) k k ∑ ∑ Infinite Impulse Response Filter k =0 k =1 N y(n) = x(k)h(n −k) ∑ Convolution k =0 N −1 X (k) = x(n) exp[ −j(2 π / N)nk] ∑ Discrete Fourier Transform n =0 N −1 π ⎡ ⎤ F() u = c(u).f (x).cos u() 2x +1 ∑ ⎢ ⎥ Discrete Cosine Transform 2N ⎣ ⎦ x =0 TU Dresden, 4/29/2008 S
Inhaltszusammenfassung zur Seite Nr. 12
Why do we need DSP processors? chair Use a DSP processor when the following are required: Cost saving Smaller size Low power consumption Processing of many “high” frequency signals in real-time Use a GPP processor when the following are required: Large memory Advanced operating systems TU Dresden, 4/29/2008 Slide 12
Inhaltszusammenfassung zur Seite Nr. 13
Hardware vs. Microcode multiplication chair DSP processors are optimized to perform multiplication and addition operations. Multiplication and addition are done in hardware and in one cycle. Example: 4-bit multiply (unsigned). Hardware Microcode Hardware Microcode 1011 1011 1011 1011 x 1110 x 1110 x 1110 x 1110 10011010 10011010 0000 Cycle 1 0000 Cycle 1 1011. Cycle 2 1011. Cycle 2 1011.. Cycle 3 1011.. Cycle 3 1011... Cycle 4 1011... Cycle 4 10011010 Cycle 5 10011010 Cycle 5 TU Dresden, 4/
Inhaltszusammenfassung zur Seite Nr. 14
General Purpose DSP vs. DSP in ASIC chair Application Specific Integrated Circuits (ASICs) are semiconductors designed for dedicated functions. The advantages and disadvantages of using ASICs are listed below: Advantages Disadvantages Advantages Disadvantages • High throughput • High investment cost • High throughput • High investment cost • Lower silicon area • Less flexibility • Lower silicon area • Less flexibility • Lower power consumption • Long time from design to • Lower power consu
Inhaltszusammenfassung zur Seite Nr. 15
Floating vs. Fixed point processors chair Applications which require: High precision Wide dynamic range High signal-to-noise ratio Ease of use Need a floating point processor Drawback of floating point processors: Higher power consumption Usually higher cost Usually slower than fixed-point counterparts and larger in size TU Dresden, 4/29/2008 Slide 15
Inhaltszusammenfassung zur Seite Nr. 16
chair TMS320C6711 Architectural Overview TU Dresden, 4/29/2008 Slide 16
Inhaltszusammenfassung zur Seite Nr. 17
General DSP System Block Diagram chair Internal Memory Internal Buses P E External R I Memory P Central H E Processing R A Unit L S TU Dresden, 4/29/2008 Slide 17
Inhaltszusammenfassung zur Seite Nr. 18
‘6711 CPU Overview chair Specification Clock Rate: 100/150 MHz 600/900 MFLOPS 0.18- μm/5-Level Metal Process – CMOS Technology CPU has got two Datapaths, altogether: Four ALUs (Floating- and Fixed-Point) Two ALUs (Fixed-Point) Two Multipliers (Floating- and Fixed-Point) Load-Store Architecture 2*16 32-Bit General-Purpose Registers TU Dresden, 4/29/2008 Slide 18
Inhaltszusammenfassung zur Seite Nr. 19
‘6711 CPU Overview chair VelociTI advanced very-long instruction words (VLIW) Program Memory Width is 256 Bit Up to 8 32-Bit instructions can be executed in parallel/Cycle 16, 32 and 40 bit fixed point operands 32 and 64 bit floating point operands Instruction parallelism is detected at compile-time no data dependency checking is done in Hardware. Instruction Packing Reduces Code Size All operations work on registers Memory Architecture 4K-Byte L1P Program Cache (Direct Mapped) 4K-
Inhaltszusammenfassung zur Seite Nr. 20
Functional Block and CPU Diagram chair TU Dresden, 4/29/2008 Slide 20
