Design and Development of Efficient Energy Systems

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There is not a single industry which will not be transformed by machine learning and Internet of Things (IoT). IoT and machine learning have altogether changed the technological scenario by letting the user monitor and control things based on the prediction made by machine learning algorithms. There has been substantial progress in the usage of platforms, technologies and applications that are based on these technologies. These breakthrough technologies affect not just the software perspective of the industry, but they cut across areas like smart cities, smart healthcare, smart retail, smart monitoring, control, and others. Because of these “game changers,” governments, along with top companies around the world, are investing heavily in its research and development. Keeping pace with the latest trends, endless research, and new developments is paramount to innovate systems that are not only user-friendly but also speak to the growing needs and demands of society.
This volume is focused on saving energy at different levels of design and automation including the concept of machine learning automation and prediction modeling. It also deals with the design and analysis for IoT-enabled systems including energy saving aspects at different level of operation.
The editors and contributors also cover the fundamental concepts of IoT and machine learning, including the latest research, technological developments, and practical applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of IoT and machine technology, this is a must-have for any library.

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Figure 211 Behavioral simulation of 8x8 VM Figure 211presents the simulated - фото 34

Figure 2.11 Behavioral simulation of 8x8 VM.

Figure 2.11presents the simulated waveform of which obtained while we implemented the 8x8 Vedic Multiplier. We have given input for a specific time, and it goes to the end till which we have mentioned stop in the test bench. Here a is 8-bit input and b is 8-bit input, and we are getting 16 bit as output, and it is represented in p. The multiplier is implemented with Verilog HDL using the Xilinx ISE tool. The synthesis result obtained from vertex-4 FPGA. An 8-bit VM requires 418 slices, 729 LUT and 67 IOB shown in Table 2.2. A similar effect is verified with cadence NCSIM and the implementation result confirmed with RTL compiler at gpdk 180nm technology. The simulation result shows that

Table 2.2 FPGA utilization summary report.

Device utilization summary
Logic utilizations Used Available Utilization
Slices 418 1672 4%
4 input LUTs 729 17344 4%
Bounded IOBs 67 250 26%

The case I Input A=8’d136 and B=8’d119 result in multiplier values p=16’h16184.

Case II Input A=8’d141 and B=8’d124 result in multiplier values p=16’h17484.

Case III Input A=8’d145 and B=8’d129 result in multiplier values p=16’h18834.

2.4 Results and Discussion

The performance parameter of 8-bit VM is judged by power consumption, delay, and area report obtained by the cadence RTL compiler. Completed implementation of an 8-bit Vedic multiplier is through the combinatorial block. The usage of the compressor reduces the requirement of resources. As the number of the logic gate but increases the interconnecting wires. Similarly, area requirements to interface multiple compressors significantly enhance the area and delay. In this section area, power and delay are presented due to logical resources and wires.

2.4.1 Instance Power

This pie chart in Figure 2.12presents the instance power used in the 8x8 Vedic Multiplier. In this chart, g1 and g2 determine compressor 10to4 and its usage of instance power is 15.79%. z1 and z2 define compressor 8to4, and its usage of instance power is 12.93%. d1 and d2 limit compressor 9to4 and its usage of instance power is 14.29%. N1 and N2 is a compressor of 7to3 whose power consumption is 8.19% of total power. M1 represents compressor 6to3, and its usage of instance power is 4.02%. V2 defines compressor 5to3, and its usage of instance power is 2.33%. Remaining all other compressors like t1 represents compressor 4to3, v1 represents compressor 5to3 and h1 defines about compressor 3to2 usage of instance power is 40.48%. An 8-bit Vedic more multiple logical blocks consume only 59.52% of total power. 40.48% of total power is consumed by another factor. The similar power distribution of the 16-bit multiplier is presented in Figure 2.13. Compressor block requires 37.78% of total power which 62.22% of total power consumed by other factors like wire.

Figure 212 Instance power usage of 8X8 vedic multiplier Figure 213 - фото 35

Figure 2.12 Instance power usage of 8X8 vedic multiplier.

Figure 213 Instance power usage of 16X16 VM 242 Net Power This pie chart - фото 36

Figure 2.13 Instance power usage of 16X16 VM.

2.4.2 Net Power

This pie chart in Figure 2.14defines the Net power used in the 8x8 Vedic Multiplier. The identification of the power-hungry block is identified from this chart. In this chart, g1 and g2 determine compressor 10to4; consumes 5.64% of the total net power. z1 and z2 define compressor 8to4, and its usage of net power is 3.72%. d1 and d2 define compressor 9to4, and its usage of net power is 4.27%. n1 and n2 define compressor 7to3, and its usage of net power is 2.29%. m1 defines compressor 6to3, and its usage of net power is 1.23%. v2 defines compressor 5to3, and its usage of net power is 0.60%. Remaining all other compressors like t1 denotes about compressor 4to3, v1 represents compressor 5to3 and h1defines about compressor 3to2 usage of net power is 82.24%. This pie chart in Figure 2.15defines the Net power distribution in the 16-bit multiplier.

Figure 214 Net power usage of 8X8 VM Figure 215 Net power usage of 16X16 - фото 37

Figure 2.14 Net power usage of 8X8 VM.

Figure 215 Net power usage of 16X16 VM 243 8Bit Multiplier Table - фото 38

Figure 2.15 Net power usage of 16X16 VM.

2.4.3 8-Bit Multiplier

Table 2.3presents the cell area requirement of the 8x8 Vedic Multiplier. Cell area report models the number of the basic cell requires to the design. To implement this project 139 cells are required, and the area which is obtained by cells is 1517, and the net area is “zero”. The total area which is accomplished is 1517, and there is no Wire Load for this Vedic Multiplier.

Table 2.3 Cell area of 8x8 vedic multiplier.

Instance Cells Cell area Net area Total area Wire load
Vedic Multiplier 139 1517 0 1517 (D)

Table 2.4 Power constraints of 8x8 vedic multiplier.

Instance Cells Leakage power(nW) Dynamic power(nW) Total power(nW)
vedicmultiplier 139 13442.07 86424.01 99866.08

Table 2.5 Time constraint of 8x8 vedic multiplier.

Pins Type Fan out Load (fF) Slew (ps) Delay (ps) Arrival (ps)
p[14] out port 0 2637 R

An instance of an 8-bit VM named Vedic multiplier requires 139 cells in this project; there is an unwanted sub-threshold current which is leakage of power in 13422.07nW (Nano Watt). Dynamic power, a part of power that consumed while the inputs are active, obtained value is 86424.01 nW and the total power which is obtained for this 8x8 Vedic Multiplier is 99866.08 nW shown in Table 2.4.

Here pin p[14] defines that it is the last output pin of 8x8 Vedic Multiplier and type defines whether it is input port or output port. The longest path arrival time (AT) of signal is calculated as the latency for a signal to arrive at point of consideration, for this project is 2637 ps shown in Table 2.5.

2.4.4 16-Bit Multiplier

The 16x16 Vedic Multiplier requires 666 cells to implement this project, and the area which is obtained by cells is 5967, and the net area is “zero” as shown in Table 2.6. The total area in which it is accomplished is 5967, and there is no wire load for this Vedic Multiplier.

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