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1. CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
A New Optimal Algorithm for Energy Saving in
Embedded System with Multiple Sleep Modes
Abstract:
For embedded systems with multiple sleep modes, it is interesting to understand how to
maximize the energy saving potential by choosing the suitable sleep mode(s) during the idle
period. In this paper, we establish a sufficient condition to narrow down the search space of sleep
policy and propose a new algorithm: optimal-idle-threshold-policy-algorithm under more
realistic setting than the existing works. Theoretical proofs and experimental results justify the
benefits of our approach. The proposed architecture of this paper analysis the logic size, area and
power consumption using Xilinx 14.2.
Enhancement of the project:
Existing System:
Irani et al. analyzed optimal offline algorithm (called lower envelope algorithm (LEA)) and their
deterministic online algorithm. Optimal offline algorithm knows idle period and the arrival time
of every task beforehand, then it is able to determine whether such idle period is long enough to
make the energy savings from staying in sleep modes outweigh the cost of transition back to the
running mode. Optimal online algorithm does not know what optimal offline algorithm knows so
that it need certain thresholds to decide if transition to sleep modes incurs more cost. The
behavior of optimal offline algorithm is in fact choosing the best assignment of power states
because of knowing the input (tasks and idle period) in advance, and the basic idea of the
deterministic online algorithm is in fact trying to mimic the behavior of the optimal offline
algorithm. Based on deterministic online algorithm, those authors further presented their
probability-based online algorithm that consists of two parts: 1) optimizing power based on a
probability distribution and 2) dynamically adjusting certain parameters by learning the
probability distribution.
Swaminathan and Chakrabarty proposed an offline algorithm, LEDES, to decide if embedded
system with two modes (one is running mode and the other is sleep mode) should transition from

2. CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
sleep mode to running mode to guarantee the timely completion of tasks and as the extension of
LEDES, the authors further proposed another offline algorithm, MUSCLES, that targets
embedded system with multiple sleep modes to decide if embedded system should transition
from one sleep mode to another to response to tasks timely. Baptiste presented an optimal offline
algorithm targeting to schedule a set of unit tasks, with release dates and deadlines, on a single
given embedded system to minimize the number of idle.
Disadvantages:
 Power consumption is high
Proposed System:
The algorithm proposed in this paper provides a more realistic setting for maximizing energy
saving for embedded system with multiple sleep modes in idle periods. In our model, the state
transition energy cost between different modes only needs to follow the triangle inequality
instead of following the symmetric assumption or being additive.
Fig. 1 presents a certain embedded system with two sleep modes. We use sr, s0, s1, and s2 to
denote the running mode, idle, the first sleep, and the second sleep, respectively.
Fig. 1. Transition diagram of embedded system with two sleep modes.
We have the following principles for an embedded system.

3. CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
1) When there are tasks to be processed, it will stay in sr.
2) When sr ends, it will select to enter s0, s1, or s2.
3) It is able to switch its states among s0, s1, and s2 during idle period.
4) Its state transition is wholly controllable to guarantee the timely completion of tasks to
avoid delays.
Fig. 2. Three possible scenarios when embedded systemhas two sleep modes. (a) Embedded systemstays in the idle
mode during the period of idle. (b) Embedded systemstays in the first sleep mode during the period of idle. (c)
Embedded systemstays in the second sleep mode during the period of idle
There are many possible ways for embedded system to switch its states during idle period. Fig. 2
shows three possible scenarios of state transition sequences for embedded system with two sleep
modes (other scenarios are presented in the Appendix). For example, Fig. 2(a) means entering s0
directly when sr ends and returning to sr before next sr comes.
OITPA ALGORITHM
We divide OITPA into offline part and online part.
Offline Part
Some parameters such as optimal thresholds should be obtained and then stored in the embedded
system before OITPA works. All related parameters except optimal thresholds (offline-part
results) may be obtained from data sheet or experimental measurement, and all the steps except
storing such offline-part outcomes may be executed by some other computing tools.
Online Part
After obtaining the optimal thresholds in offline part and I with the help of BIP, we formally list
OITPA now.
Purpose of OITPA: OITPA shows that when embedded system owns some features , the number
of action sequences may be decreased by omitting the sleep policies that switching states among

4. CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
different sleep modes during idle period. Most energy saving action sequences only contain
simple sequences.
Analysis of Overhead and Latency
Overhead of OITPA consists of offline part and online part. As for online-part overhead, in
offline part, except optimal thresholds, we can resort to data sheet to get the other related
parameters. Then, we can execute the procedure in Algorithm 1 via other computing tools except
storing such thresholds in the embedded system, which means the workload for embedded
system only includes storing its thresholds, so offline-part overhead comes from optimal
thresholds storage.
Advantages:
 Save the energy
Software implementation:
 Modelsim
 Xilinx ISE