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Embedded Systems: Enabling Technologies and Applications

Explore the evolution and impact of embedded systems, from smart dust to microcontrollers, in various devices from phones to appliances. Learn about emerging trends, challenges, and opportunities in the embedded systems field.

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Embedded Systems: Enabling Technologies and Applications

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  1. Embedded Systems: Enabling technologies and roadmap, promising applications, research opportunities Frank Vahid and Walid Najjar Dept. of Computer Science and Engineering University of California, Riverside NOES meeting, January 2004 Frank Vahid, UC Riverside

  2. Key Technology Trend • Moore’s Law: 2x every 18 months • ~10,000 transistors in 1980 • Nearly 1 billion transistors today 10,000 1,000 100 Logic transistors per chip (in millions) 10 IC capacity Source: ITRS’99 1 0.1 0.01 0.001 1995 1997 1999 1985 1987 1993 2003 2005 2009 1981 1983 1989 1991 2001 2007 Frank Vahid, UC Riverside

  3. 1981 1984 1987 1990 1993 1996 1999 2002 10,000 transistors 150,000,000 transistors Leading edge chip in 1981 Leading edge chip in 2002 Key Technology Trend • Graphical illustration of Moore’s Law • Two trends • Smaller and cheaper • More functions Frank Vahid, UC Riverside

  4. Smaller and cheaper chips • 32-bit microprocessor, plus networking, in tiny packages • E.g., “Smart dust” • Mass-produced microcontrollers • E.g., PICs for <$1 today, likely pennies in the future “Smart dust” Getting awfully small... Photo courtesy of Joe Kahn Frank Vahid, UC Riverside

  5. Computers Everywhere -- Today 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 A “short-list” of embedded systems • A computing system embedded within an electronic product • whose primary function is not a computer • 98% of processors are embedded [Tu02] • 40-50 in every home • >50 in some cars • Annual sales • Embedded processors alone >$3 billion (Dataquest’00) • Other ICs >$20 billion (Gartner/Dataquest 01) 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 Frank Vahid, UC Riverside

  6. Computers everywhere – today Frank Vahid, UC Riverside

  7. Computers everywhere -- tomorrow • In every hallway? • In every electric appliance? • In every food package? • In every shirt? • In every tooth? Frank Vahid, UC Riverside

  8. Garage Door Open at Night (wireless solution) wireless TX Light Sensor wireless RX Outside LED 2-Input Logic Magnetic Contact Switch Inside house At garage door Smaller and cheaper – UCR’s “eBlocks” • Sensors, logic, communication, and output blocks • No programming or electronics experience • 2-3 year battery life • Solution: • Every block has computer, communication via packets • Future: autoconfiguration? • Countless possible uses • NSF-funded • Vahid, Najjar, Hsieh Frank Vahid, UC Riverside

  9. Magnetic Contact Switch yes/no yes/no LED yes/no Splitter yes/no yes/no yes/no yes/no Light Sensor yes/no yes/no Button yes/no yes/no Toggle yes/no yes/no yes/no 2-Input Logic Defining Basic eBlocks – Partial Catalog Frank Vahid, UC Riverside

  10. Output B A A Logic from motion sensor no no no yes B from light sensor no yes no yes yes no no yes yes yes no yes Defining Basic eBlocks – How to Implement Logic? • Logic to detect motion at night • Motion sensor output A = yes, light sensor output B = no • Motion at night = A AND (NOT B) = A * B’ • Equations too hard for regular person, plus how to enter it? • In general, regular people very weak with logic • Can’t create unique eBlock for every unique 2-input logic function • Create one 2-input logic block • User must configure that block • Solution: print truth table on block, user sets switches for each possible input • Not ideal, but sufficient for now Frank Vahid, UC Riverside

  11. Programmability • 100s of sensors in one network • Write 100s of C programs? • Or, write one description of functionality and GENERATE 100s of programs. • Which language can express the system design? • Simulation? • Impractical at this scale • Verification is the only option Frank Vahid, UC Riverside

  12. 1981 1984 1987 1990 1993 1996 1999 2002 10,000 transistors 150,000,000 transistors Leading edge chip in 1981 Leading edge chip in 2002 Key Technology Trend • Graphical illustration of Moore’s Law • Two trends • Smaller and cheaper • More functions Frank Vahid, UC Riverside

  13. More functions -- present SDRAM ctrl • Entire systems on one chip • Philips Viper: high-quality audio and video • 1,920 x 1,080 interlaced pixels • Processes multiple input streams • Renders, composes and outputs video and audio output streams • Tremendous data-processing demands MIPS CPU TriMedia CPU Int ctrl MPEG2 dcd 2D rend JTAG image proc. PCI Clocks video proc. I^2 C IC debug other proc. 1394 CPU debug Digital I/O CRC DMA USB Audio I/O UART Other I/O Frank Vahid, UC Riverside

  14. More functions – future? • Hundreds of processors on a single chip? Frank Vahid, UC Riverside

  15. 10,000 100,000 1,000 10,000 100 1000 Logic transistors per chip (in millions) Gap Productivity (K) Trans./Staff-Mo. 10 100 IC capacity 1 10 0.1 1 productivity 0.01 0.1 0.001 0.01 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 More functions – PROBLEM! • Designer productivity growing at slower rate • 1981: 100 designer months  ~$1M • 2002: 30,000 designer months  ~$300M Design productivity gap Moore’s Law Source: ITRS’99 Frank Vahid, UC Riverside

  16. Trend Towards Pre-Fabricated Platforms • Example -- ASSP: application specific standard product • Domain-specific pre-fabricated IC • e.g., digital camera IC • ASIC: application specific IC • ASSP revenue > ASIC • ASSP design starts > ASIC • Unique IC design • Ignores quantity of same IC • ASIC design starts decreasing • Due to strong benefits of using pre-fabricated devices Source: Gartner/Dataquest September’01 Frank Vahid, UC Riverside

  17. Single-Chip Microprocessor/FPGA Platforms • Altera’s Excalibur EPXA 10 (2002) • ARM (922T) hard core • 200 Dhrystone MIPS at 200 MHz • ~200k to ~2 million logic gates Source: www.altera.com Frank Vahid, UC Riverside

  18. Single-Chip Microprocessor/FPGA Platforms • Xilinx Virtex II Pro (2002) • PowerPC based • 420 Dhrystone MIPS at 300 MHz • 1 to 4 PowerPCs • 4 to 16 gigabit transceivers • 12 to 216 multipliers • Millions of logic gates • 200k to 4M bits RAM • 204 to 852 I/O • $100-$500 (>25,000 units) • Up to 16 serial transceivers • 622 Mbps to 3.125 Gbps PowerPCs Config. logic Courtesy of Xilinx Frank Vahid, UC Riverside

  19. New generation of languages and compilers needed • Languages to specify functionality of hugely complex systems • Built largely from existing functions • Compilers to map functionality to existing hardware resources • Multiple processors, plus FPGA • How convert to functionality to custom circuit on FPGA? • While considering performance, power, and size criteria Frank Vahid, UC Riverside

  20. Advanced DSP Simple CPU or DSP FPGA Fabric * * * * * * * + + + + + 1 MAC 1 tap/cycle + + RC fabric – custom circuit K taps/cycle 2 MACs + 1 ALU 2 taps/cycle Advantage of compiling to hw: Parallelism • Parallelism limited by chip area or memory bandwidth • With enough bandwidth: 100s of iterations! Frank Vahid, UC Riverside

  21. UCR: Some Performance Results PC: 800 MHz Pentium III, FPGA: Xilinx Virtex E 2000, all FPGA programs compiled from SA-C with extensive compiler optimizations Ref: W. Najjar et al., IEEE Computer August 2003 Frank Vahid, UC Riverside

  22. Compilers for future platforms • UCR research • Synthesis of C to hw circuits • Automatic partitioning of program among sw and hw • From C/C++/Java source, or even binaries • 10x-100x speedups by mapping sw kernels to hw • Warp processors – same, but dynamic and transparent • Vahid, Najjar, Hsieh, Tan • Funding: NSF, SRC (Semiconductor Research Corp), Los Alamos, Northup Gruman, others… • How can we support platforms with hundreds of microprocessors and huge FPGAs? • Also, how utilize processor “soft cores” that can be mapped to the FPGA too? Frank Vahid, UC Riverside

  23. Directions • Smaller and cheaper trend • Languages to specify desired system behavior • Synthesis tools to generate hw and sw • Power optimization • More functions trend • Languages/compilers for multi-billion-transistor platforms having hundreds of processors and huge FPGAs Frank Vahid, UC Riverside

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