1. How you made your generator capable of handling the flexibility required by the parameters (k, p, b, g)? Were any of these particularly easy or difficult to support? How did you handle the fact that there were no maximum defined values on k and b? Are there practical limits on these values? If so what are they and why?
Hardware generation is a tool which generates an instance for certain given parameters of the design. Scalability and flexibility are two important factors that bring practical limits to such tools. In this case, a hardware generator tool generates the instance of Matrix Vector Multiplication hardware with parameters matrix dimension (k), parallelism coefficient (p), input data word length (b) and pipelining coefficient (g). It has a Controller module that deals the timing of the Datapath module which does the MAC calculations and stores the data. The controller module is basically an FSM with some finite number of states. As the parameter changes, the number of state has to change in order to maintain the timing to get correct output. Hence, flexibility of the tool mainly relies on Controller Module’s flexibility.
In the present generator, the number of states can be calculated and executed on the basis of parameter k. The tool can also support parallel designs as it can instantiates the datapath module in the top module depending upon the parameter p. The datapath module has a flexibility to be pipelined or unpipelined. Input data word length b determines the size of registers in and memory storage word size. Typically, it is easy with the flexibility of parameters like g and b, but in case of p and k, the code has to change significantly in order to cater the timing requirement.
Conceptually, there are n...
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... more efficient as it can compute large calculation in small time due to more number of units working simultaneously. But the size of the design has to increase due to additional hardware.
(c)Data word length, b: As b increases the size of the memory and associated registers also increases.
(d)Pipelining, g: Pipeline will add registers or flipflops in the design but it will reduce the delay and hence the computational period.
6. Use your generator to produce four designs with b = 4, 8, 16 and 20 with k = 8, p=8 and g=1. Produce two graphs (a) power versus b and (b) area versus b for these designs. Describe the critical path of the design.
Following are the two graphs that represent power and area with respect to b respectively. The critical path is from Memory X -> Multiplier -> Pipelined register between Multiplier and adder -> output memory Y -> Multiplexor
Number of steps: A synthetic route which requires fewer steps results in lower utilization time of equipment
Figure 2 shows the implementation of FMULT_ACCUM. Inputs to the FMULT block are two’s complement and floating point parallel input. Output of this block is two’s complement 1 bit serial output. A four bit Count_in signal is used as a counter which avails FMULT to repeat operations after 16 iterations. Clear_Accum and Clear_Carry signals are used to reset accumulator. Done_Signal is used to reset the counter. The reason to choose 146 will be discussed in the accumulator section. These logics are implemented using finite state machine.
Increasing the power output is meant to increase the overall megawatts produced by the company. With increased megawatts, the company will be in a position to distribute power to more consumers and hence increase its resources. The Ohio project...
MANEKA WEERASINGHE - BSBPMG522 - TASK 1 - DETAILED PROJECT PLAN Project 1 (part B)
The machine has 16 general purpose 8-bit registers numbered 0 through F (in hexadecimal). Each register can be identified in an instruction by specifying the hexadecimal digit that represents its register number. Thus register 0 is identified by hexadecimal 0 and register 10 is identified by hexadecimal A.
The most significant of these are the Large Capital Cost of these systems themselves, and the noticeable value of recoverable electricity generated. The other key factors include the pressure drop and the gas flow rate, which combined determine the power production potential, and variability in flow.
... to shorten the amount of time I spent waiting for a query to finish. The biggest improvements came form using indexes and joins. Using an index made it so the queries had a sort of reference when searching and performing operations on specific columns. Joins decreased the amount of operations the system had to process and made the biggest difference in run time. As databases grow it is important to try and reduce the over all cost by optimizing queries.
...ories, and equations that were introduced in the math chapter in which I struggled, were used in that lab. I was able to see how we can collect data presented by a graph and turn it into an equation and vice versa which in turn will help me to decide the capacity of the electrical source that I need to accomplish a certain operation.
Figure 1 represents the microarchitecture of a typical superscalar processor. It includes instruction fetching and branch prediction, decode and register dependence analysis, issue and execution, memory operation analysis and instruction reorders and commit.
b. Statistical Modeling: Decide about which distribution pattern a data set follows. It eases the descriptive analysis and provides foundation for predictive analysis.
Finally, we can calculate the simple design load (Dead Load, Wind Load, Live Load) base on the preliminary drawing.
OMPARE AND CONTRAST REDUCED INSTRUCTION SET COMPUTER (RISC) WITH THE COMPLEX INSTRUCTION SET COMPUTER (CISC) [20]
Dadda proposed an algorithm with predetermined sequence of matrix (stage) heights for NxN multipliers to have reduced number of reduction stages. It is developed by working back from two row stage. The height of each intermediate stage is limited to floor value of 1.5 times the height of the successor stage [9].
SOC applications usually have varying memory requirements. Memory structure can be residing entirely inside the chip in one case, and for another case the memory system might support an elaborate operating system requiring a large off-chip m...
Von Neumann architecture, or the Von Neumann model, stems from a 1945 computer architecture description by the physicist, mathematician, and polymath John von Neumann and others. This describes a design architecture for an electronic digital computer with a control unit containing an instruction register and program counter , external mass storage, subdivisions of a processing unit consisting of arithmetic logic unit and processor registers, a memory to store both data and commands, also an input and output mechanisms. The meaning of the term has grown to mean a stored-program computer in which a command fetch and a data operation cannot occur at the same time because they share a common bus. This is commonly referred to as the Von Neumann bottleneck and often limits the performance of a system.