A Novel Custom Topology Generation for Application Specific Network-on-chip Using Genetic Algorithm Optimization Technique
In Networks-on-chips (NoC), the main sources of power consumption are global
interconnection links and routers. In Application Specific NoC (ASNoC) power
can be minimized by mapping the cores on the application specific topology (custom
topology) rather than mapping on the standard topologies. In ASNoC, the design
of the topology plays an important role in minimizing the power consumption
and hop count. In this study, we propose a novel topology generation algorithm
using genetic algorithm optimization technique to generate a custom topology
for ASNoC architectures. We applied the proposed algorithm to six benchmark
video applications MPEG 4 decoder, VOPD, MWD, mp3 audio encoder, mp3 audio decoder
and DSP. The proposed topology generation algorithm achieves significant amount
of power saving and decrease in the average number of hop count compared to
the existing custom topology generation algorithms.
Received: October 17, 2012;
Accepted: November 10, 2012;
Published: January 14, 2013
In the last years, with the technology scaling down, the number of processors,
DSPs, memory elements and Intellectual Property (IP) cores on a System on Chip
(SoC) is increasing more (Srinivasan et al., 2005).
This rapid increase in the number of components on a single chip has resulted
in increased computation and communication complexity. Hence, todays SoC
requires new on-chip communication medium that are energy efficient and have
high performance (Benini and De Micheli, 2002). Networks-On-Chip
(NoC) has come out as a feasible solution for designing energy efficient and
high performance communication architectures, for Multi Processor SoC (MPSoC)
(Lai and Lin, 2012). In NoC, the design of the topology
affects the performance, power consumption and overall area of the on chip.
Hence to minimize the power consumption, application specific topology is to
be designed rather than mapping on the standard topologies like mesh, star.
In modern Application Specific NoC (ASNoC), different communication requirement
exists between different cores. For these ASNoCs, standard topologies would
result in poor performance and large overhead of power consumption and area.
This requires application specific topology which requires few resources like
routers than standard topologies to minimize the area and power consumption
(Lai and Lin, 2012).
In NoC, routers are placed to reduce the length of interconnect. IP cores communicate
through routers. The number of routers the data bits travel to reach the destination
determines the power consumption. The more the number of routers the data bits
travel, the higher is the total power consumption. Hence, we must construct
application specific topology in such a way that the number of hops or routers
the data bits travel to reach the destination is reduced (Lai
and Lin, 2012). This is achieved by having high communicating cores placed
close to each other. This creates shorter communication distance between high
communicative cores. This reduces power consumption and increases the performance,
as the message bits travel fewer routers to reach the destination (Deepak
et al., 2009). In this study, we propose a novel topology generation
algorithm using genetic algorithm based optimization technique that is capable
of generating custom topology to a specific application for reduction in the
power consumption compared to regular topologies.
In recent years, many research works have been carried out in the design of
the application specific custom topology. Lai and Lin (2012)
used genetic algorithm for the generation of custom topology. The authors consider
core to router mapping instead of core to port mapping considered Leary
et al. (2009) in the initial population. This saves the search path.
The authors (Lai et al., 2010) use Dijkstras
shortest path algorithm to find the route in the final topology generation.
Throughput oriented custom topology is generated by using two algorithms (Dumitriu
and Khan, 2009) and their topology is compared with standard topologies
like Mesh and Fat tree and another custom topology. Four various heuristics
based algorithms are proposed (Deepak et al., 2009)
for generating the optimal tree based topology for multimedia applications.
Power analysis and the energy consumption are performed using Orion power modeling.
Murali and de Micheli (2004a) proposed mapping and routing
of the cores onto NoC standard topology using unified approach. Pande
et al. (2005) proposed an evaluation methodology to compare the performance
and characteristics of a variety of standard NoC architectures like SPIN, CLICHÉ,
Torus, Folded torus, Octagon, BFT. A prototype implementation of the NoC architecture
for multimedia applications in FPGA and ASIC is carried out (Ogras
et al., 2007). The authors also evaluated the power and the area,
both in FPGA and ASIC implementation. They do not attempt to evaluate and compare
the performance of their custom NoC architecture with standard architectures.
The SCOTCH partitioning tool is used to map complex application task graph onto
four architecture (Bononi et al., 2007) and analysis
is performed by OMNET++ simulator. Chang and Chen (2008)
proposed the power aware topology construction method which is used to construct
Application Specific low power interconnection topologies based on the traffic
characteristics of SoC. The authors analyze their low power algorithm using
C++ based simulator and have port constrain for router and use only
the four port router and their algorithm is compared with mesh topology only.
Atienza et al. (2008) proposed a complete NoC
Synthesis flow. They compare several multimedia applications with the standard
mesh topology and their custom topology, in terms of area and power for nanometer
regime. Elmiligi et al. (2009) proposed a partitioning
algorithm to generate hybrid topology and apply for the MPEG 4 decoder. The
authors analyze and compare the performances of the NoC like power, hop count
and the number of links with the standard topologies and hybrid topologies.
Ar et al. (2009) generate custom topology using
port constraint router for benchmark video applications and they compare their
work with the existing algorithm. The authors generate (Srinivasan
et al., 2005) custom topology for ASNoC using polynomial algorithms
and applied for several benchmark video applications. Number of clusters is
determined using Genetic Algorithm based optimization Technique (Vijendra
et al., 2012). An efficient and powerful design method for calculating
optimal Proportional-integral-derivative (PID) controllers for AVR systems is
proposed using genetic algorithm (Mohammadi et al.,
2009). Wei et al. (2007) proposed a hybrid
routing protocol to reduce the delay in mesh network. A new GA based task scheduling
technique is proposed (Akhtar, 2007) and is tested on
a multi-node grid environment and the results show that this method can lead
to significant performance gain in various applications network load balancing
Chiu (2010) proposed an efficient path selection algorithm
using genetic algorithm. A cloud based manufacturing resources service problem
is proposed (Ding et al., 2012). Four ports routers
are used by Chang and Chen (2008) and Ar
et al. (2009) for topology generations of ASNoC, only 2 or 3 cores
are connected to a single router. This constrain forces them to use more number
of routers. In contrast to these works, in this study, we propose a three phase
novel topology generation algorithm that uses a router in which the numbers
of ports are parameterizable. In our proposed work the number of ports can be
parameterized up to seven. The increase in the number of ports in a router from
4 ports to seven ports increases the router crossbar size which in turn increases
the power consumption of the single router. Although the power consumed by the
single router is increased the overall router power consumption in the topology
is reduced. This is because, the number of routers used for the given applications
is reduced compared to the works proposed (Chang and Chen,
2008; Ar et al., 2009). To assess the efficiency
of the proposed algorithm, we applied the proposed algorithm to few benchmark
video applications namely MPEG 4 decoder, VOPD, MWD, mp3 encoder, mp3 decoder
and DSP. The custom topology generated by the proposed algorithm uses lesser
number of routers than the other custom topologies generated by the existing
topology generation algorithms. This reduces area, power consumption compared
to the previous works.
POWER ANALYSIS IN NoC
Power consumption is the main restriction factor in NoCs. In NoC, the power
consumption is due to two components: (1) router and (2) the interconnect wires.
These interconnect wires consume noteworthy amount of power (Vitkovski
et al., 2008). A brief power analysis is given for the NoC router
and the interconnection links.
Power analysis of NoC router: In NoC router, power dissipation takes
place on three components: (1) the internal node switches, (2) the internal
buffers used to store the flits temporarily. (3) the interconnect wires inside
the switch (Ye et al., 2002).
The design of the network router (Murali and Gopalakrishnan,
2011) consists of four parts: (1) input port (2) arbitration unit (3) crossbar
switch and (4)output port. The basic router unit is given in the Fig.
1. The input port has buffers to store the data and a header decoder unit
to decode the destination address.
|| Router architecture
||Comparison of (a) Area and (b) Power of the router on TSMC
0.18 μm technology for different ports numbers
We employ wormhole flow control to reduce the switch buffering and to reduce
the latency. We apply round robin arbitration. Multiplexer and De-multiplexer
were used as crossbar switch. When the flits arrive at the input port, the flits
are stored in the buffer and the header unit decodes the destination address
from the header flit. It sends the request to the output port. When more than
one input ports request the same output port, the arbiter in the output port,
gives access to one input port at a time in round robin fashion. The input port
that receives the access sends the flits through the crossbar switch to the
output port. The packet size is 256 bits (16 flits each of 16 bit width). The
input buffer size is 8 flits depth and wormhole switching is used.
We examined the area and the power consumption of the NoC router, by designing the five port router in Virolog HDL and implemented on TSMC 0.18 μm technology using cadence tool. Then we customized the router architecture, by changing the no of ports in the routers as 2, 3, 4, 5, 6, 7 and 12 based on the requirement of the topology and examined the area and the power consumption for all the router architectures.
The results of area occupancy and power consumption are shown in Fig. 2. When the number of ports in the router is increased, the area occupied and the power consumed by the router increases proportionally. This is because, increase in more ports to a single router results in larger decoding logic. The crossbar must handle increased number of interconnect which increases crossbar size. For example the crossbar switch for the router having number of ports up to four can be implemented in an efficient manner using a 4 to 1 MUX. However, for designing crossbar for the router having more than four ports, the crossbar must be designed using 8 to 1 MUX or combination of large number of smaller MUXs. This is the reason for the increase in the crossbar size of the router when number of ports per router is increased.
Power analysis of NoC links: The most important sources of power dissipation
in on chip interconnections are: (1) the coupling capacitance between the two
neighboring wires and (2) the capacitance between wire and ground (Vitkovski
et al., 2008). In NoC Platform, two distinct links are considered.
They are: (1) The link between routers to router (Global link) and (2) The link
between PE to router (semi global link). The power consumption on interconnection
wires between two neighbor routers is given as in Eq. 1:
where, C is the interconnection wire capacitance, α is the average switching
activity in the wires, f is the frequency, Vdd is the supply voltage
(Vitkovski et al., 2008). The switching activity
α is defined as the ratio of the number of bits changing from 0>1 or
1>0 to the total number of bits, passed from one end to another end of a
interconnection wire over a period of time. The values of C, Vdd
and f depend on the process technology. But, the value of switching activity
α depends on the data passed through the wires. Hence, power saving is
achieved by reducing the switching activity on the wires (Vitkovski
et al., 2008).
The overall objective of this novel topology generation algorithm is to generate
a custom topology for ASNoC to reduce the power consumption and to reduce the
hop count. The proposed algorithm can be used at the early stage of the topology
design. The idea for generating low power custom topology is to form clusters
of the cores. To do this, the cores are clustered based on the communication
volume (traffic) between them. The cores that have larger communication volume
(traffic) are grouped to the same cluster to form localization. This localization
creates shorter communication distance between the cores that have larger communication
volume and reduces the average number of hops between sources to destination.
We connect one router for one cluster. The number of ports in the router is
selected based on the number of cores in the cluster. When a core is connected
to a router, the data enters through one of the input ports of the router and
leaves one of the output ports of the routers. The two ports are considered
as one hop. When larger communication cores are connected to a single router
it minimizes the hop count, this in turn minimizes the total energy consumption.
Hu and Marculescu (2003a) proposed the average energy
dissipation to send one bit of data from tile vi to vj
where, Esbit is the Energy consumption of a router, Elbit is the energy consumption of interconnecting wires, nhops is the number of routers the bit passes.
To formulate this problem more formally, we propose the following definitions
as proposed by Murali et al. (2006).
Definition 1: The Communication Task Graph (CTG) is an unidirected Graph, G (V, E), where each vertex viεV represents a core and the directed edge wijεE represents the communication volume between two cores vi and vj.
Definition 2: The Router Communication Graph (RCG) is a fully connected graph with m vertices, where m is the number of clusters. The edge weights are set to zero.
The proposed topology generation algorithm can generate topology for ASNoC
with heterogeneous routers in which the number of input/output ports is parameterizable.
In the proposed topology generation algorithm, the router ports can be parameterizable
up to seven. The number of input/output ports in a particular router in the
final topology depends on the number of cores in the clusters. The bandwidth
constrain of the router Br is assumed to be 3200 MB sec-1 (assuming
the router is operated at 200 MHz and the port width is 16 bit). We prefer the
cluster to have number of cores less than or equal to six. Sethuraman
(2007) used multi local port router to reduce the number of routers used
in the topology. The use of multi local port router increases the router performance
and reduces the number of routers used in the topology which in turn reduces
the overall power consumption in the topology. However, when the number of ports
in the router is increased largely, the router size will be bulkier as it has
to handle large amount of data and in turn the power consumption of a single
router will be increased. Hence, to get better performance of the router and
at the same time only small increase in the router size, we select the routers
to have number of ports less than or equal to seven. Leaving one port for interconnection,
we select the cluster size is less than or equal to 6 cores. For example, if
the number of cores in a cluster is 3, we use 4 ports router for that cluster
and if the number of cores in a cluster is 6, we use 7 ports router for that
cluster. Therefore the proposed algorithm can support heterogeneous router architectures.
Table 1 compares the overall router power consumption for
the proposed algorithm with the previous works. The power consumption values
are obtained from TSMC 0.18 μm technology. The proposed algorithm uses
routers up to seven ports. But the works (Chang and Chen,
2008; Ar et al., 2009) use only four ports
routers. As shown in the table, a single seven port router has more power consumption
than a single four port router. But the overall router power consumption is
reduced in the topology as the number of routers required is reduced when seven
ports router is used. In the following sub section, we present the proposed
three phase topology generation algorithm.
The proposed algorithm: The proposed topology generation algorithm has three phases. They are: (1) Construction of clusters (2) optimization of clusters using GA (3) topology generation. The proposed algorithm can be used in the early stage of the topology design.
Topology generation algorithm:
||Communication Task Graph (CTG), G(V,E)
Phase I: Construction of clusters:
||Arrange the communication volume (or) traffic between the
cores (edge weights) in descending order. Find out the number of clusters
||Maximum communication volume
||Minimum communication volume
|| Comparison of the total router power consumption for MPEG
||Find upper limit communication volume for each cluster
||(ii) Assign the cores that have communication volume in the range (U_limit[x-1]+1:U-limit[x])
to cluster m to cluster 2 as shown below:
||for x = m; x≤2; x--
||cluster [x] = (U_limit[x-1]+1:U_limit[x])
||(iii) Assign the cores that have communication volume in the range (Emin
:U_limit) for cluster 1 as shown Below. If the cores are already
assigned in one cluster neglect it.
cluster = (Emin:U_limit)
Phase II: Cluster optimization using GA and generation of RCG:
||The initial clusters formed creates unbalanced traffic in
the clusters. Hence, the clusters are optimized using GA such that
Wxij is the communication volume between the cores vi and vj in the xth cluster. Br is the bandwidth constraint of the router and m is the number of clusters:
Such that unbalanced traffic is avoided among the routers.
dx = Sx-Sx+1
If x+1>m, then x+1 = 1:
||The communication volume between the cores vi and
vj in the xth cluster
||The communication volume between the cores vi and vj
in the x+1th cluster
where, m is the number of clusters.
Then to generate RCG, the routers with different number of ports are selected based on the number of cores present in the clusters and the number of clusters. Finally the routers are interconnected to form RCG.
Phase III: Topology generation:
||The final topology is generated by connecting the cores in
the clusters to the ports of the routers in the RCG such that each core
is connected to only one port of the router
Phase I: Construction of clusters: In the first phase of the proposed algorithm, clustering of cores is completed. The number of clusters is decided based on the number of cores in the application. In the proposed algorithm, n defines the number of cores in the CTG, m defines the number of clusters. The main idea behind the construction of the cluster is to group the cores that have larger communication volume (traffic) in the same cluster. This is done by arranging the communication volume in descending order and grouping them into the clusters. Construction of the clusters is shown in step 1 to step 3 of the proposed algorithm.
Phase II: Optimization of the clusters: In the second phase of the proposed algorithm, optimization of cores is done and RCG is built. The clusters are optimized using Genetic Algorithm (GA) based optimization technique.
GA based optimization: A GA is based on the biological fact of the genetic
evolution. It keeps a set of solutions known as populations. GA operates in
an iterative manner and develops to new solutions from the current solutions
by the application of genetic operators (Leary et al.,
2009). Our GA applies crossover and mutation to produce new solutions. In
crossover two solutions are combined to generate a new solution and in mutations
an existing solution is modified to generate a new solution. Application of
two point crossover in cluster array is shown in Fig. 3.
Population representation: GA based optimization technique requires
a representation of the population for the application of genetic operators
(Leary et al., 2009). We set the population
size is equal to the number of cores n multiplied by the number
of clusters m. The total population size is divided equally among
the clusters. In each cluster the population is represented as strings of chromosomes
in an array and is called as cluster array.
|| Crossover in cluster array
We form m cluster arrays. The cluster array is a binary array where
a 1 in a location denotes the presence of the core in that cluster
and a 0 denotes that the core is not present in that cluster. For
example in a cluster array a 1 in 5th location represents the core
5 is present in that cluster and a 0 in 9th location denotes core
9 is not in that cluster.
Criteria for solution selection: Since a cluster has number of cores less than or equal to six, the criteria to be satisfied in the cluster array is that the number of 1 sec in the cluster array is less than or equal to six. We apply two point cross over to produce the solutions. Only the solutions that satisfy the above criteria are considered for the fitness calculation. The fitness constraints are given by Eq. 3 and 4.
Once the clusters are optimized, the RCG is built by selecting the routers based on the number of cores in the clusters and the number of clusters. The router ports are determined based on the number of cores in the cluster and the number of routers is determined based on the number of clusters. The selected routers are interconnected to build RCG.
Phase III (topology generation): In the third phase, final topology is generated by connecting the cores in the clusters to router ports in RCG and then interconnecting the routers. The main goal of the proposed algorithm is to minimize the power consumption and the hop count.
The custom topology generated by the proposed algorithm minimizes the number of hops the bit travels to reach the destination core and the energy consumed by a single bit which in turn minimizes overall energy consumption as given in Eq. 2. Overall energy consumption is minimized because most of the bits travel within the router.
The proposed algorithm also reduces the number of routers used in the topology. This in turn reduces area and the power consumption of the overall topology design. We apply the proposed algorithm to six benchmark video applications MPEG 4 decoder, VOPD, MWD, mp3 audio encoder, mp3 audio decoder and DSP. We will explain the steps of the proposed algorithm for benchmark video application MPEG 4 decoder. Figure 4 shows the CTG for MPEG 4 decoder.
||CTG for MPEG 4 video decoder. Edge values are communication
requirement given in Mb sec-1
|| Arrangement of communication volume in descending order
|| Initial cluster formation
|| Optimized clusters
||The first step of the proposed methodology is to arrange the
communication volumes (or) traffic between the cores of the CTG in descending
order as shown in Table 2. For MPEG 4 decoder, the number
of cores n is 12 and the number of cluster m is (since
we have maximum of 6 cores in a cluster). From the Table 1
we assign Emax = 910 MB sec-1 and Emin
= 0.5 MB sec-1
||In step 3, we find out the values for the array U_limit[x] using .
Here, x = 1, 2. Hence, U_limit = 455 MB sec-1 and U_limit
= 910 MB sec-1. The cores that communicate with communication
volume in the range U_limit+1 to U_limit are grouped in cluster 2
and cores that communicate with communication volume in the range Emin
to U_limit are grouped in cluster 1. Table 3 shows
the initial cluster formation
||In step 4, we optimize the clusters using genetic algorithm optimization
technique. Crossover and mutation are applied to the cluster array to produce
the new solutions. The cluster array is formed based on the presence of
the core in the cluster. The solution that satisfies the above said fitness
function is selected as the optimized cluster. In MPEG 4 decoder application,
there are 12 cores present. Two clusters are formed and each cluster has
6 cores. Table 4 shows the optimized cluster. We use seven
ports router for both the clusters. The RCG is shown in Fig.
|| Router communication graph (RCG) for MPEG 4 decoder
|| Custom topology generated for MPEG 4 decoder by the proposed
||The cores in the clusters are connected to the ports of the
routers in RCG and final topology is generated. The generated topology for
MPEG 4 decoder application is shown in Fig. 6.
We present the results obtained by the application of the proposed algorithm on benchmark video applications. We first give details about the benchmark video applications, the experimental setup and finally we present the results.
Benchmark applications: We have applied the proposed custom topology
generation algorithm for two multimedia applications namely mp3 audio encoder,
mp3 audio decoder. We obtained the CTG for these applications from Hu
and Marculescu (2003b). In addition to that we have also applied the proposed
algorithm to four benchmark video applications namely MPEG 4 decoder, VOPD,
MWD and DSP. The CTG for these applications were obtained from (Jalabert
et al., 2004) and the CTG for DSP is obtained from (Murali
and de Micheli, 2004b).
Experimental setup: We used the power consumption model for 100 nm technology
given (Srinivasan et al., 2005). The power consumption
for the input port, output port and for the link are estimated to be 328, 65.5
and 79.9 nW/Mbps, respectively. We estimated the power consumption for the custom
topology generated by applying the proposed algorithm for the above said applications
using the model given (Srinivasan et al., 2005).
We first applied the proposed algorithm for the benchmark applications to generate
the custom topologies. We compare the power consumption for the custom topologies
generated by applying the proposed algorithm with the existing topology generation
algorithms. The results of the proposed algorithm and the existing topology
generation algorithms are given in Table 5. The proposed algorithm
achieves significant power saving compared to the existing algorithms.
|| Comparison of power consumption for benchmark applications
We also compare the average hop counts of the topology generated by the proposed
algorithm with the average hop count of the standard mesh topology for all the
benchmark applications. We achieve 5.75x improvement in the average hop count
compared to standard mesh topology.
In this study, we presented a novel custom topology generation algorithm using genetic algorithm optimization technique to generate the custom topology for application specific Network on chips. There are three phases in the proposed topology generation algorithm. They are: (1) Construction of clusters (2) Optimization of clusters and (3) Topology generation. In the first phase, we group the cores that communicate with larger communication volume (traffic) in the same cluster. In the second phase, the clusters are optimized suing genetic algorithm optimization technique such that each cluster has number of cores less than or equal to six, bandwidth constraint of the routers are satisfied and the traffic among the clusters are balanced. Then RCG is built. In the final phase, custom topology is generated by connecting the cores in the cluster to the router ports and interconnecting the routers. The main goal of the proposed algorithm is to minimize the power consumption and the average hop count. We evaluated the performance of the proposed algorithm for six benchmark applications namely MPEG 4 decoder, VOPD, MWD, mp3 audio encoder, mp3 audio decoder, DSP by comparing with the existing topology generation algorithm. The proposed algorithm achieves significant amount of power reduction compared to the existing algorithm and average hop count compared to the standard mesh topology.
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