Automated electronic design By Alexander Mitev 1 2

Automated electronic design By Alexander Mitev 1. 2. 3. 4. 5. Introduction Area of problems Approaches and solutions Other related Ideas Conclusions

Introduction • - Why to automate ? • - Time cycles, cost, quality • - Computer design vs. hand made

Introduction • - Digital vs. Analog DIGITAL ANALOG LARGE SMALL VHDL SCHEMATIC Design SYSTEMATIC INTUITIVE Synthesis system CURRENTLY USED NOT YET SIMPLE COMPLEX HIGHER? ? ? Circuit scale Design methodology Behavior of the transistors Salary

Introduction • The art of analog circuit design

Area of problems • • • Complex behavior of analog circuits No common rule how to synthesize The imperfection of computer modeling The rising complexity of formal solutions The highest degree of freedom of analog circuits • Mostly the solutions in analog design is approved after simulation or real test.

Available solutions – Genetic programming -Achieves this goal of automatic programming by genetically breeding a population of computer programs using the principles of Darwinian natural selection and biologically inspired operations. -Genetic operations include reproduction, crossover (sexual recombination), mutation, and architecture-altering operations patterned after gene duplication and gene deletion in nature. -The main generational loop of a run of genetic programming consists of the fitness evaluation, Darwinian selection, and the genetic operations.

Available solutions – Genetic programming -Genome – a collection of genes , representing parameters of the problem to be optimized - Example of genome:

Available solutions – Genetic programming, example 1 • Goal – Best solution for the transistor widths and resistor value for R 1 • Genome – 76 bit , approximately 7. 5 x 1025 possible designs

Available solutions – Genetic programming, example 1 • Results: • Result in 163’th generation • Total work time: Pentium 200 - 15 min

Available solutions – Genetic programming, example 2 • Goal – Filter Synthesis • Limitation: bandwidth, minimal component • Fitness calculation:

Available solutions – Genetic programming, example 2 Best individual at generation 40

Available solutions – Genetic programming, example 2 - Solution after the 100’th generation - Minimal elements and exact pass bandwidth. - Total CPU time - 7 h 43 min.

Available solutions – Genetic programming, example 2

Available solutions – Genetic programming, example 3 Goal Low pass filter using parallel GA - Genome 400 bytes ; 18000 individuals max - Best result 23’th generation - Total working time 6 SUN workstation 4 hours -

Available solutions – Genetic programming: Summary -Benefits: -Novel approach by simulating natural selection -Mostly with GA could be solved problems faster than by hand. Shortcoming (regarding this research) -Time dependable -Domain dependable, presently available for filters and amplifiers -No guarantee to solve the problem

Available solutions – Automated design by reusing • CBR: the basic idea is to solve a new problems by comparing them with old ones that have been solved in the past • Major method – testing for similarity. • Base with “past experience”, Intelligent retrieval • Intelligent retain – if a new solution is available • Generalizing this idea by considering the common sense of Intellectual product (IP)

The reuse - cycle - Retrieval – an IP is selected corresponding to the respective specifications

The reuse - cycle - Retrieval – an IP is selected corresponding to the respective specifications - Instantiation: If an IP has been selected the necessary design is instantiated, OR

The reuse - cycle - Retrieval – an IP is selected corresponding to the respective specifications - Instantiation: If an IP has been selected the necessary design is instantiated, OR - The design flow is defined to meet the constraints

The reuse - cycle - Retrieval – an IP is selected corresponding to the respective specifications - Instantiation: If an IP has been selected the necessary design is instantiated, OR - The design flow is defined to meet the constraints - Retain – if it has been decided to store the synthesized circuit

Solution – Reuse

Design by reuse • Algebraic description of Libraries • Basic requirement : LB, LF library Kb – behavior to be synthesized Vb – behavior parameters Db- allowed parameters Kf – design flow to be synthesized Vf – des. flow parameters Df- allowed des. flow parameters

Design by reuse LB library – parameterized design LB – store certain design, which can be instantiated Kb – parameterized specification of the synthesized behavior Vb – specification parameters Db – allowed parameters

Design by reuse • • Specifications b and b’ are: - identical - equal (same behavior) - extension b<b’ • Example 32 bit adder and 16 bit adder

Design by reuse • LF library – design flow for synthesis LF – store certain design flow Kf – parameterized design flow Vf – specification parameters Db – allowed parameters values

Design by reuse • Algebraic description of Libraries • Reuse library LIB Ki – parameters for behavior and des. flow Vi – both parameters from Vb and Bf Di- allowed parameters values

Design by reuse • Reuse component IP I = (Ki, Vi, Di) • Instance if |Di|=1 • Configurable if |Di|>1 Ki – parameters for behavior and des. flow Vi – specification parameters from Vb and Bf Di- allowed parameters values

Design by reuse

Design by reuse • Similarity of IP’s For each parameter in Vi is calculated similarity of the allowed parameters Rv – relevance factor, stating the importance ov V for the hole design

Design by reuse • Constraint management

Design by reuse - example • READEE project – CBR technology of design and IP reuse • Realization as a WEB based service. • Problem domain – selection of DSP processors • Similarity measure – application and DSP-specific information ( Example : consuming power ) • Performance or qualifying is based on abstract table model including many application orientated properties

Design by reuse - example • DSP operations table: • 1. Operations , such as arithmetic, logic etc. • 2. Performance profile : filter (parameters) ; FFT (parameters, #simples) ; general purpose options • Task of evaluation model supporting similarity calculations: • 1. Clock frequency is derived as a function of consuming power • 2. User specified performance profile is mapped to the main data code.

Design by reuse - example DSP function taxonomy

Design by reuse - summary Benefits: - Knowledge based approach - No iterations, quick solution (if there is a solution) - With retain mechanism – theoretically provide solutions for all possible problems Drawbacks: 1. Design for reuse 2. Numerous parameters, one another dependable 3. In many cases scalable design is impossible 4.

Design by reuse • Define domain of problem set (ex. Noise, gain etc. ) • Determine all domain of parameters ( ex. frequency, resistance, etc). V – V [p 1, …. pn] • Level-1: L 1 base - Separate the Analog circuit in simple modules with known parameters (ex. Transistor, capacitor etc). L 1 - L 1[Ti, Vi] • Leve-2 : L 2 base – Topological low level description (CE transistor, Differ. Pair. ) L 2 – L 2(L 1 k, Vk)

Design by reuse • Hence we have description of all analog building blocks • The instantiate base describes all available design circuits • Now we can reuse different block according problem specification

Design by reuse Simplifying of circuit by substitution with building blocks

Ideas for automated electronic design • By decomposition the circuit into submodules or building blocks we can get these advantages: • 1. instantiate each block, not the hole circuit • 2. if we symbolic describe behavior of each block we can make some prediction of the total behavior of the circuit. More important is the reverse operation – to make a relation from behavior to building modules.

Ideas for automated electronic design • 3. If we can make a complete set of building blocks. It will be one step ahead to use one special case of reuse – software reuse similar to HDL based languages. Presently is available analog extension of VHDL but only for elementary elements ( transistors, diodes) • Problems : – Rising complexity of symbolic representation with the size of circuit – Every circuit maintain one symbolic representation, but not vice versa.

Conclusion, feature work 1. The of analog key the designdistinguishing is in of the topologies. on 2. the topic of automated analog design). Example: Hi-Fi audio amplifier includes: filters, amplifying modules, comparators etc). 3. Each element of this ontology has to be provided with detailed parametric specifications: - allowed parameters ; - all scalable parameters. 4. We can reuse the elements not the hole circuit

Conclusion, feature work 3. 4. Scenario for design flow could be possible to simulate based on the symbolic description (expression) of the problem by evaluating and/or reusing. Mechanism for decomposition major problem to minor subproblems.