1. ACOE343 - Real-Time Embedded Processor Systems Dr. Konstantinos Tatas Office 107, FRC building http://staff.fit.ac.cy/com.tk

2. Course outline (1/5) Programme of Studies:BSc in Computer Engineering, BSc in Computer Science Name of the module:ACOE343 - Real- Time Embedded Processor Systems Target group: Computer Engineering – Computer Science students Level of the unit:BSc –6th Semester Entrance requirements: ACOE201 Number of ECTS credits: 6

3. Course outline (1/5) Competences to be developed: –Produce efficient real-time designs using the latest technology of PIC and 8051-based microcontrollers and the state-of-art Texas Instruments TMS320C64x DSP processors. –Demonstrate real-time algorithmic design techniques for embedded applications using assembly and C/C++ programming, implementation, hardware debugging and measurement techniques using the available uP and DSP boards.

4. Course outline (1/5) Course Description: Embedded C/C++ and Assembly Languages: Embedded C and assembly for programming the 8051-based microcontrollers. Coverage of C/C++ for programming DSPs. Real-Time Design Techniques. 8051-Based Microcontrollers: The MSC121x Development System, Real-Time Input and Output Applications, Architecture and ISA of the MSC121x microcontroller, Real-Time Embedded Ethernet Applications and Real-Time Data Acquisition Applications. DSPs: The DSP Development System, Real-Time Input and Output Applications with the DSK, Architecture and ISA of the C64x Processor, Fixed-Point Considerations. DSP Techniques: FIR and IIR filter Applications. FFT, digital modulation techniques and Applications. Laboratory Work: Individual or small group experiments based on using a variety of EDA tools for programming, debugging and testing the microcontroller and DSP boards.

5. Course outline (1/5) Textbooks: –M. Pont, Embedded C, Addison Wesley, 2002. –N. Kehtarnavaz, Real-Time Digital Signal Processing Based on the TMS320C6000, Newnes, 2004. –K. Arnold, Embedded Controller Hardware Design, Newnes, 2001. References: –M. Margolis, Arduino Cookbook, O’Reilly –T. Noergaard, Embedded Systems Architecture: A Comprehensive Guide for Engineers and Programmers, Newnes, 2005. –B. DeMuth, Designing Embedded Internet Devices, Newnes, 2002. –L. Edwards, Embedded System Design on a Shoestring, Newnes, 2003. Course webpage: –http://staff.fit.ac.cy/com.tk/ACOE343.html

6. Course outline (1/5) Assessment: –Mid-term Exams: 40% –Laboratory Work: 40% –Assignment/Group project: 15% –Quizzes: 5%

7. Mid-term exams (40%) Microcontrollers: –Multiple choice questions –Small programs Assembly/C/either DSPs: –Multiple choice questions –Small programs Mostly C

8. Laboratory work (40%) Small group experiments with the ChipKit MAX32 PIC- based Arduino Small group experiments with EdSim 8051 emulator Small group experiments with the TI DSK Deliverables: –Lab report (with source code)!!! Assessment: –Active participation:40% –Methodology/Source code:30% –Board Testing: 20% –Presentation:10% Labs should be submitted within one week after lab conduct time and grades will be posted on the e-learning website within another week

9. Assignment/Group Project (15%) 8051 emulator or Chipkit MAX32 application implementation Done in small groups Different application for each group

10. Microprocessors for Embedded systems Computing systems are everywhere Most of us think of “desktop” computers –PC’s –Laptops –Mainframes –Servers But there’s another type of computing system –Far more common...

11. Embedded systems overview Embedded computing systems –Computing systems embedded within electronic devices –Hard to define. Nearly any computing system other than a desktop computer –Billions of units produced yearly, versus millions of desktop units –Perhaps 50 per household and per automobile Computers are in here... and here... and even here... Lots more of these, though they cost a lot less each.

12. A “short list” of embedded systems And the list goes on and on Anti-lock brakes Auto-focus cameras Automatic teller machines Automatic toll systems Automatic transmission Avionic systems Battery chargers Camcorders Cell phones Cell-phone base stations Cordless phones Cruise control Curbside check-in systems Digital cameras Disk drives Electronic card readers Electronic instruments Electronic toys/games Factory control Fax machines Fingerprint identifiers Home security systems Life-support systems Medical testing systems Modems MPEG decoders Network cards Network switches/routers On-board navigation Pagers Photocopiers Point-of-sale systems Portable video games Printers Satellite phones Scanners Smart ovens/dishwashers Speech recognizers Stereo systems Teleconferencing systems Televisions Temperature controllers Theft tracking systems TV set-top boxes VCR’s, DVD players Video game consoles Video phones Washers and dryers

13. Some common characteristics of embedded systems Single-functioned –Executes a single program, repeatedly Tightly-constrained –Low cost, low power, small, fast, etc. Reactive and real-time –Continually reacts to changes in the system’s environment –Must compute certain results in real-time without delay

14. An embedded system example – Digital camera Single-functioned -- always a digital camera Tightly-constrained -- Low cost, low power, small, fast Reactive and real-time -- only to a small extent Microcontroller CCD preprocessorPixel coprocessor A2D D2A JPEG codec DMA controller Memory controllerISA bus interfaceUARTLCD ctrl Display ctrl Multiplier/Accum Digital camera chip lens CCD

15. Embedded Software Development Requires as Much/More Design Effort Than Hardware

16. A System-on-a-Chip: Example Courtesy: Philips

17. Design at a crossroad System-on-a-Chip RAM 500 k Gates FPGA + 1 Gbit DRAM Preprocessing Multi- Spectral Imager  C system +2 Gbit DRAM Recog- nition Analog 64 SIMD Processor Array + SRAM Image Conditioning 100 GOPS Embedded applications where cost, performance, and energy are the real issues! DSP and control intensive Mixed-mode Combines programmable and application-specific modules Software plays crucial role

18. Design challenge – optimizing design metrics Obvious design goal: –Construct an implementation with desired functionality Key design challenge: –Simultaneously optimize numerous design metrics Design metric – A measurable feature of a system’s implementation –Optimizing design metrics is a key challenge

19. Design challenge – optimizing design metrics Common metrics –Unit cost: the monetary cost of manufacturing each copy of the system, excluding NRE cost –NRE cost (Non-Recurring Engineering cost): The one-time monetary cost of designing the system –Size: the physical space required by the system –Performance: the execution time or throughput of the system –Power: the amount of power consumed by the system –Flexibility: the ability to change the functionality of the system without incurring heavy NRE cost

20. Design challenge – optimizing design metrics Common metrics (continued) –Time-to-prototype: the time needed to build a working version of the system –Time-to-market: the time required to develop a system to the point that it can be released and sold to customers –Maintainability: the ability to modify the system after its initial release –Correctness, safety, many more

21. Design metric competition -- improving one may worsen others Expertise with both software and hardware is needed to optimize design metrics –Not just a hardware or software expert, as is common –A designer must be comfortable with various technologies in order to choose the best for a given application and constraints SizePerformance Power NRE cost Microcontroller CCD preprocessorPixel coprocessor A2D D2A JPEG codec DMA controller Memory controllerISA bus interfaceUARTLCD ctrl Display ctrl Multiplier/Accum Digital camera chip lens CCD

22. Time-to-market: a demanding design metric Time required to develop a product to the point it can be sold to customers Market window –Period during which the product would have highest sales Average time-to-market constraint is about 8 months Delays can be costly Revenues ($) Time (months)

23. Losses due to delayed market entry Simplified revenue model –Product life = 2W, peak at W –Time of market entry defines a triangle, representing market penetration –Triangle area equals revenue Loss –The difference between the on-time and delayed triangle areas On-time Delayed entry Peak revenue Peak revenue from delayed entry Market rise Market fall W2W Time D On-time Delayed Revenues ($)

24. Losses due to delayed market entry (cont.) Area = 1/2 * base * height –On-time = 1/2 * 2W * W –Delayed = 1/2 * (W-D+W)*(W- D) Percentage revenue loss = (D(3W- D)/2W 2 )*100% Try some examples –Lifetime 2W=52 wks, delay D=4 wks –(4*(3*26 –4)/2*26^2) = 22% –Lifetime 2W=52 wks, delay D=10 wks –(10*(3*26 –10)/2*26^2) = 50% –Delays are costly! On-time Delayed entry Peak revenue Peak revenue from delayed entry Market rise Market fall W2W Time D On-time Delayed Revenues ($)

25. Real-time (reactive) systems Systems that are bound by a real-time constraint (“deadline”) in their operation If the deadline is not met it is usually considered a system failure, even if the output is eventually correct Deadlines are usually relative to an event Hard deadlines: Anti-lock brakes, Soft deadlines: Digital video Not the same as high-performance systems, because often running faster than real-time requirement is not necessary or desired

26. Real-time constraints te + to < tc te: execution time to: overhead time tc: constraint time

27. Example Assuming a real-time system that processes samples at a f= 10 MHz sampling rate, and a to= 20 ns, select the most appropriate implementation among the following: –A processor running at 500 MHz, requiring 100 cycles at a cost of 50$ –An FPGA running at 200 MHz, requiring 10 cycles at a cost of 60$ –A DSP running at 500 MHz, requiring 20 cycles at a cost of 100$ –An ASIC running at 2 GHz, requiring 20 cycles at a cost of 500$