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CS302 - Digital Logic Design

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  • CS302 GDB1 Solution and discussion

    Solved cs302 gdb 1 solution discussion fall 2020
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    zaasmiZ
    @Huzaifa-Asif said in CS302 GDB1 Solution and discussion: Re: CS302 GDB1 Solution and discussion Total Marks 5 Starting Date Thursday, February 18, 2021 Closing Date Friday, February 19, 2021 Status Open Question Title PAL vs PLA - Gaded Discussion Board (GDB) Question Description CS302 – Digital Logic Design Graded Discussion Board Suppose you had reduced a 32-variable Boolean expression using Quine–McCluskey algorithm to a 12-variable expression. For the generated simplified expression, you are required to implement it into a digital logic circuit. You can only use Programmable Array Logic (PAL) or Programmable Logic Array (PLA) devices. Assume that we had selected a Programmable Array Logic (PAL) and a Programmable Logic Array (PLA) for you to choose between. Using TICPAL22V10Z-25C (Programmable Array Logic) Using PLUS173–10 (Programmable Logic Array). Your selections among stated PLA and PAL must consider the following constraints: Complexity Flexibility Speed Functionality Cost Important instructions for GDB submission: You must provide a precise and to the point answer. Your answer should be no more than 5 to 6 lines and do avoid irrelevant details. Post your answer on the Graded Discussion Board (GDB), GDB through email or MDB will not be accepted in any case. GDB will only be open for 48 hours, no more time or grace day will be provided. Any answers copied from the internet or other student will get zero marks. Implementing a 12-variable Boolean expression derived from a complex 32-variable reduction requires a careful balance between architectural flexibility and hardware efficiency. Given the high number of inputs (12) and the likely high density of product terms (given it originated from 32 variables), the choice between the TICPAL22V10Z-25C and the PLUS173–10 is critical. Comparison Analysis Feature TICPAL22V10Z-25C (PAL) PLUS173–10 (PLA) Architecture Programmable AND, Fixed OR Programmable AND, Programmable OR Complexity Simple; easier to program. High; both arrays are programmable. Flexibility Limited; fixed number of OR gates per output. High; product terms can be shared across outputs. Speed Faster (single programmable array delay). Slower (double programmable array delay). Efficiency Can waste logic if many terms are needed. Highly efficient for dense logic. Selection Criteria Based on Constraints 1. Complexity & Flexibility The PLUS173–10 (PLA) is the superior choice here. Because your expression originated from a massive 32-variable space, the resulting 12-variable simplified version likely still contains many common product terms. The PAL has a fixed OR-plane, meaning if one output requires more product terms than the PAL’s hardware allows (usually 8–16 per output), the design fails. The PLA allows product term sharing. If multiple parts of your expression use the same logic, the PLA can reuse a single AND gate for multiple OR gates. 2. Speed The TICPAL22V10Z-25C (PAL) wins on raw speed. Because the OR-plane is fixed (hardwired), the signal propagation delay () is significantly lower. If your digital circuit is part of a high-speed processor or timing-critical interface, the PAL’s 25ns rating (indicated by the “-25C”) is a predictable advantage. 3. Functionality The TICPAL22V10Z-25C is a “22V10” architecture, meaning it has 12 inputs and 10 outputs with “Variable” product term distribution. This matches your 12-variable requirement perfectly. However, if the Quine–McCluskey reduction resulted in an expression with a high “sum-of-products” count that exceeds 10–16 terms for a single output, the PAL will physically not be able to compute the function. 4. Cost Generally, PAL devices are more cost-effective for mass production and simpler logic because the manufacturing process for a single programmable plane is cheaper than a dual programmable plane. Final Recommendation Choose the PLUS173–10 (PLA). Reasoning: While the PAL is faster, the complexity of a 12-variable expression reduced from 32 variables suggests a high likelihood of “product term heavy” logic. A PLA provides the necessary flexibility to map complex Boolean reductions without hitting the “fixed-OR” ceiling of a PAL. In a 32-to-12 variable reduction, logic density is usually a bigger bottleneck than nanosecond-level propagation speed.
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  • CS302 Assignment 1 Solution and Discussion

    Solved cs302 assignment 1 solution discussion spring 2020
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    zaasmiZ
    Download CS302 Assignment Solution
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    zareenZ
    @zareen said in CS302 Assignment 3 Solution and Discussion: Design the final Circuit diagram. A circuit diagram is a graphical representation of an electrical circuit. A pictorial circuit diagram … Circuit diagrams are used for the design (circuit design), construction (such as PCB layout), and maintenance of electrical and electronic … This results in the final layout artwork for the integrated circuit or printed circuit board. Reff [image: L3kBbVH.png]
  • CS302 Assignment 2 Solution and Discussion

    Solved cs302 assignment 2 solution discussion fall 2019
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    zareenZ
    ASSIGNMENT NO:2 Course: CS302 1: Write the SOP expression for the given sum. Sol: A B C D E 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 1 0 0 1 1 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 1 1 0 1 1 0 0 0 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1 0 0 0 0 1 0 0 0 1 1 0 0 1 0 1 0 0 1 1 1 0 1 0 0 1 0 1 0 1 1 0 1 1 0 1 0 1 1 1 1 1 0 0 0 1 1 0 0 1 1 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 A B C D E OUTPUT (F) 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 0 1 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 1 0 0 1 1 0 0 1 0 1 1 0 1 0 0 1 1 1 0 1 0 1 1 1 1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 0 1 1 0 1 0 1 0 0 1 1 0 1 0 1 0 1 0 1 1 0 1 1 0 1 1 1 0 1 1 0 0 0 1 1 1 0 0 1 0 1 1 0 1 0 1 1 1 0 1 1 0 1 1 1 0 0 1 1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 0 FOR SOP WE FOCUS ON 1 VALUE. A B C D E MINTERM 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 1 0 0 1 1 0 0 0 1 1 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 1 1 0 0 1 1 0 0 0 1 1 0 1 0 1 1 1 0 0 1 1 1 1 1 ABCDE SOP EXPRESSION: SUM OF PRODUCT EXPRESSION 2: Find Prime Implicant of minterm using QuineMcculsky method. Step-1 00010 2 00100 4 01000 8 10000 16 00110 6 01010 10 01100 12 10010 18 10100 20 11000 24 01110 14 10110 22 11010 26 11100 28 11110 30 Step-2 2,6(00-10) 2,10(0-010) 2,18(-0010) 4,12(0-100) 4,6(001-0) 4,20(-0100) 8,10(010-0) 8,24(-1000) 16,18(100-0) 16,20(10-00) 16,24(10-00) 6,14(0-110) 6,22(-0110) 10,14(01-10)10,26(-1010) 12,14(011-0) 12,28(-1100) 18,26(1-010) 18,22(10-10) 20,22(101-0) 20,22(1-100) 24,26(110-0) 24,28(11-00) 14,30(-1110) 22,30(1-110) 26,30(11-10) 28,30(111-0) Step-3 2,6,18,22(-0-10) 2,6,10,14( 0–10)2,10,18,26(–010)2,18,6,22(-0-10) 2,18,10,26(–010)4,12,6,14(0-1-0) 4,6,12,14 (0-1-0) 4,6,20,22(-01-0)4,20,6,22(-01-0) 4,20,12,28(–100) 8,10,12,14(01–0) 8,24,10,26(-10-0) 8,24,12,28 (-1-00) 6,14,22,30 (–110) 6,22,14,30(–110) 10,14,26,30(-1-10) 10,26,14,30(-1-10) 12,14,28,30(-11-0) 12,14,28,30(-11-0) 18,26,22,30(1–10) 18,22,26,30(1–10) 20,22,28,30(1-1-0) 20,22,22,30(1-1-0) 24,26,28,30(11–0) 24,28,26,30(11–0) Step-3 2,6,18,22(-0-10) 4,6,20,22(-01-0) 2,18,10,22(-0-10) 4,20,6,22(-01-0) 2,10,18,26(–010) 4,12,6,14(0-1-0) 2,18,6,22(–010) 4,6,12,14 (0-1-0) 4,20,12,28(–100) 10,14,26,30(-1-10) 6,14,22,30 (–110) 10,26,14,30(-1-10) 12,14,28,30(-11-0) 18,26,22,30(1–10) 12,14,28,30(-11-0) 18,22,26,30(1–10) 20,22,28,30(1-1-0) 24,26,28,30(11–0) 20,22,22,30(1-1-0) 24,28,26,30(11–0) 6,22,14,30(–110) 8,10,12,14(01–0) 8,24,12,28 (-1-00) 8,24,10,26(-10-0) Step-4 2,6,8,22(-0-10) 4,6,20,22(-01-0) 2,10,18,26(–010) 4,12,6,14(0-1-0) 4,20,12,28(–100) 10,14,26,30(-1-10) 12,14,28,30(-11-0) 18,26,22,30(1–10) 20,22,28,30(1-1-0) 24,26,28,30(11–0) 6,22,14,30(–110) 8,10,12,14(01–0) 8,24,12,28 (-1-00) 8,24,10,26(-10-0) These are the prime implicates cs302-assign 2.docx
  • CS302 GDB1 Solution and discussion

    Solved cs302 gdb1 solution discussion spring 2019
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    M
    The large energy cost of memory fetches limits the overallefficiency of applications no matter how efficient the ac-celerators are on the chip. As a result the most importantoptimization must be done at the algorithm level, to reduce off-chip memory accesses, to createDark Memory. The algorithmsmust first be (re)written for both locality and parallelism beforeyou tailor the hardware to accelerate them.Using Pareto curves in theenergy/opandmm2/(op/s)spaceallows one to quickly evaluate different accelerators, memorysystems, and even algorithms to understand the trade-offsbetween performance, power and die area. This analysis isa powerful way to optimize chips in the Dark Silicon era.
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