The document discusses improving the performance of digital systems. It notes that digital filter design is important for optimizing speed, area, and energy efficiency. It

1.  In the current phase of the advancement in the field of
digitalization, the device modeling is going to be accurate and
energy efficient with high speed and less complexity .
 So it is very important that the designing of digital system
should be fast, easily adaptable(less complex and compatible),
and energy efficient with low cost.
 Therefore digital filter designing is the most prominent area of
the DSP system design and its research to improve the
performance of the digital systems in terms of speed, area and
energy.
 Adaptive filters have the great advantage of self-adjusting their
transfer functions according to the optimized algorithms. They
are powerful adaptive systems with numerous applications in
the fields of signal processing, communications, radar, sonar,
seismology, navigation systems and biomedical engineering.
1/8/2023
Anuj Kr. Sharma, Research Scholar – ECE, UTU 1

2. 1/8/2023
Anuj Kr. Sharma, Research Scholar – ECE, UTU 2

3.  The LMS (least mean squares) algorithm is an approximation of the
steepest descent algorithm which uses an instantaneous estimate of
the gradient vector of a cost function. The estimate of the gradient
is based on sample values of the tap-input vector and an error
signal.
1/8/2023
Anuj Kr. Sharma, Research Scholar – ECE, UTU 3

4. Y(n)=w’(n).x(n)
e(n)=d(n)−y(n)
W(n+1)=w(n)+μe(n)x(n)
D-LMS Adaptation Algorithm:
 Direct implementation of LMS leads to have the large critical
path and high adaptation delay.
 For high performance computing, critical path and adaptation
delay should be minimum.
 Therefore pipeline structure is required to meet the
requirement of high performance.
 So LMS needs to modified as it does not support the pipleline
structure because of recurssivness process,after modification it
is known as D-LMS Algorithm.
1/8/2023
Anuj Kr. Sharma, Research Scholar – ECE, UTU 4

5.  NMSE –Normalized Mean Square Error
Basic Design of FIR Filter
Algorithms
SSP Model
 N - Order or Computational Complexity
Filter Structure – Systolic and Pipeline
In case of adaptive FIR filter : LMS, Critical Path ADP,EPS etc.
1/8/2023
Anuj Kr. Sharma, Research Scholar – ECE, UTU 5

6.  Tried to compare the results with SPW-Synopsis.
 Communicate Two Papers on the research work carried out in
the phase.
 Course work-2 is completed and waiting for the result.
1/8/2023
Anuj Kr. Sharma, Research Scholar – ECE, UTU 6

7. 1/8/2023
Anuj Kr. Sharma, Research Scholar – ECE, UTU 7

8. Algo Arch CP M A tadp
D-LMS Systolic 2tM+ ntA 2n 2n 1
Ramnathan Folded SA tM + tA 2n/p + 1 2n/p + 1 n/Mp + (2p1+p2 +3) /3
Ven & Feng Systolic tM + tA 2n+1 2n n/4 +3
Ting al Fine Grained tA 2n 2n Log2 n +5
Meher Systolic tA 0 9n+1 4
1/8/2023
Anuj Kr. Sharma, Research Scholar – ECE, UTU 8

9.  To compete the research work and prepare the thesis Work for
the submission in next RDC.
 Planning to submit in next RDC.
 To deliver the state of art seminar in next RDC
1/8/2023
Anuj Kr. Sharma, Research Scholar – ECE, UTU 9

10. Thank You
1/8/2023
Anuj Kr. Sharma, Research Scholar – ECE, UTU 10