ACO-BASED FAULT-AWARE ROUTING ALGORITHM FOR NETWORK-ON-CHIP SYSTEMS

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1 2013 IEEE Workshop on Signal Processing Systems ACO-BASED FAULT-AWARE ROUTING ALGORITHM FOR NETWORK-ON-CHIP SYSTEMS Chia-An Lin, Hsien-Kai Hsin, En-Jui Chang, and An-Yeu (Andy) Wu Graduate Institute of Electronics Engineering, National Taiwan University No. 1, Sec. 4, Roosevelt Road, Taipei, Taiwan (R.O.C) ABSTRACT With the shrinking size of circuits and the scaling of Network-on-Chip (NoC), the on-chip components will have a higher chance to fail. The on-chip failures can cause traffic congestion and even system crash. To overcome this problem, the NoC routing algorithm should be implemented with fault-tolerant capability. Inspired by the fault-tolerant behavior of ant colony consisting of three steps: Encounter, Search, and Select, we propose Ant Colony Optimizationbased Fault-aware Routing (ACO-FAR) algorithm for traffic balancing. To effectively forward the packets to a non-faulty region, three mechanisms of ACO-FAR correspond to the three-step behaviors of ants are proposed in this work. The simulation results show that proposed ACO-FAR has higher throughput than related works by 12.5%-77.7%. Also, this routing method improves the reachable packet ratio to 99.50%-99.98% and the distribution of traffic load in the faulty network. Figure 1. Fault-tolerant scheme in NoC system. faulty part of routers [3]. Then, the fault-tolerant routing algorithms [4-7] can detour the faulty routers based on the fault information while keeping the packet transmission complete. However, the resulting low path diversity causes the unbalanced network traffic. That is, the traffic loads are congested around the faulty node, and the system performance degraded drastically. Hence, for further improvement of system performance, the fault-tolerant routing algorithms need to be designed with higher path diversity and traffic balance ability. ACO-based adaptive routing was proposed for NoC traffic balance in [8]. ACO is a bio-inspired algorithm that mimics the process of an ant colony in finding the shorter path from nest to food. It can reduce the traffic congestion by the usage of ant pheromone as the network historical information and achieve traffic balance effectively. According to [9], ant colony also shows the faulttolerant behavior. It consists of three steps, 1) encounter the obstacle, 2) search for available paths, and 3) select of the better path. By regarding fault routers as obstacles and packets as ants, the condition is the same in the network. Therefore, inspired by this behavior of ants, we propose an ACO-based fault-aware routing algorithm (ACO-FAR) with three techniques. The main contributions of this paper include the following: 1) Notification mechanism of fault information: We propose provides a low-cost dynamic notification mechanism to effectively propagate fault information in the network. 2) ACO-based fault-aware path searching mechanism: To provides as much path diversity as possible, this Index Terms Ant Colony Optimization, Network-onChip, Fault-tolerant Routing, Fault-aware Mechanism 1. INTRODUCTION For Multiprocessor System-on-Chip (MPSoC), Network-onChip (NoC) provides flexible, reliable, and scalable on-chip communication architecture and has advantages of low latency and high throughput [1]. However, with the development of semiconductor technology, the density of on-chip components increases. The defective transistors and failure in interconnections become more serious [2]. Moreover, with the scaling up of the system, the on-chip components have a higher chance to fail. Unfortunately, these failures cause the unbalanced traffic load and even system crash. Thus, fault-tolerant approaches are critical for building reliable systems and increasing the product yield. In the recent years, many NoC fault-tolerant schemes have been proposed [2]. These schemes consist of fault detection mechanism and fault-tolerant routing algorithm, as shown in Fig.1. Firstly, the fault detection mechanism uses the particular circuit to detect, locate, and isolate the /13 $ IEEE 342

2 mechanism searches for all possible paths to neighboring nodes except for faulty paths. 3) ACO-based fault-aware path selecting mechanism: To relieve the traffic congestion around the faulty nodes, this mechanism can be aware of faulty nodes and select the better path with ant pheromone. Besides, such fault awareness, can make the system performance degrade gracefully. 2. RELATED WORKS In recent years, many fault-tolerant routing algorithms have been proposed to deal with faulty routers in NoC. Generally, there are two main categories of these algorithms: 1) turn model based [4-6] and 2) virtual channels (VCs) [7] based fault-tolerant routing algorithms. 2.1 Turn Model Based Fault-tolerant Routing The turn model based fault-tolerant routing algorithms [4-6] place restrictions on the routing function. The restrictions prohibit particular turns next to the faulty region. In addition, these methods may have some assumptions on the location of faults. However, these routing algorithms only provide the robustness under certain circumstances. Moreover, the system performance drastically degrades due to lower path diversity and traffic imbalance. In contrary, an ACO-based fully adaptive routing has high potential to efficiently divert traffic to less congested areas and improve performance. 2.2 Virtual Channels Based Fault-tolerant Routing The VCs based fault-tolerant routing algorithms [7] release parts of routing restrictions from the turn model based approaches by using VCs, which allows multiple transactions to share a single physical channel in time multiplexing. However, for the resource-limited NoC system, the hardware cost of routers is a critical issue. Due to high area cost and power consumption of VC, VCs-based routing is unacceptable in the resource-limited NoC. Therefore, in this paper, we propose an ACO-based routing without using VCs to achieve fault-tolerance. 3. PROPOSED ROUTING ALGORITHM According to [9], the fault-tolerant behavior of an ant colony consists of three steps, 1) encounter, 2) search, and 3) select. In Fig. 2(a), firstly, when the obstacle appears, ants on the pheromone trails encounter with it and cannot move forward directly. Then, they search for other available paths by random directions to detour from the obstacle. After a short period, the shorter path continues to accumulate pheromone. Finally, ants select the better path to pass through. Figure 2. (a) Fault-tolerant behavior of an ant colony. (b) Corresponding routing process of the proposed algorithm. 3.1 ACO-based Fault-Aware Routing (ACO-FAR) By regarding faulty routers as obstacles and packets as ants, similar condition is the same in the network. With this assumption, we propose three schemes according to ants three-step behavior to improve fault-tolerance ability and tightly combine with ACO-based adaptive routing [8]. In general, adaptive routing determines the suitable output channel for each packet based on network status. It consists of a routing function and selection function. The routing function gives a set of candidate channels, and the selection function chooses one proper output channel based on the network information, such as output queue length. We modified this routing scheme by adding fault information for achieving fault-tolerance. The path selecting mechanism and the path selecting mechanism are corresponding to the routing function and the selection function, respectively. They route packets to proper output direction using the fault information in fault-awareness process. The proposed routing process is shown in Fig. 2(b) and discussed below Notification mechanism of fault information First of all, in order to add the fault information to the routing algorithm, we propose a mechanism that collects and propagates the information of faults. This mechanism can make the fault-aware routing decision more efficient. A fault detection mechanism is generally implemented in the router, to locate the faulty position of the routers. The faulty routers are disabled from transmitting packets. Then these routers send Fault Regional Index (FRI) signals to the neighboring routers. Due to the limited resource in the NoC system, we implement the FRI as a local signal for minimizing the cost to provide fault information. We also makes the FRI adjustable and scalable for different network sizes. We set 343

3 the FRI as an n-bit local signal, where n is a value that subjects to the network size. Figure 3. (a) The propagation of FRI (b) The corresponding reaction of receiving different values of FRI. For example, in Fig. 3(a), we assign the FRI as a 2-bit local signal for an 8 8 mesh NoC. When the fault is detected by the fault detection mechanism, the FRI value of signals connecting to the adjacent routers at the faulty router are set to 3, which is the maximum value for a 2-bit signal. This value decreases when propagated to the adjacent hop until it reduces to zero. This means that the fault information can propagate to at most 4-hops away. Moreover, for the multiple faults situation, the value of FRI is obtained by the maximum value of receiving FRI signals from adjacent routers in order to evaluate the worst-case fault condition, as shown in (1). 3, max,,, 1, (1) The path searching mechanism and the path selecting mechanism react correspondingly depending on the FRI value received, as listed in Fig. 3(b). Hence, the FRI mechanism can bring the traffic load away from the faulty node and reduce the congestion of nearby routers. This greatly alleviates the problem of performance degradation ACO-based fault-aware path searching mechanism When receiving FRI signals from neighboring routers, the router can identify whether its neighboring routers are normal or faulty. The path searching mechanism can then provide appropriate candidate output channels to the path selecting mechanism. The path searching mechanism searches for all possible paths to adjacent nodes except for faulty paths to provide higher path diversity. There are three common cases shown in Fig. 4 to illustrate this process: 1) Case I: A packet at the source node is sent to the destination node. The path searching mechanism provides fully-minimal paths (i.e., North and East) for candidate channels since the faulty node is not adjacent to the source node (receiving FRI does not equal to 3). 2) Case II: The situation is similar to Case I except for the faulty node is adjacent to the node sending the packet (receiving FRI equals to 3). As a result, the candidate channel provided by the path selecting is North and meanwhile the pheromone of East channel is also set to zero. 3) Case III: The only minimal path from source to destination is blocked by the faulty node. Therefore, there are no possible minimal paths to transmit. Hence, the path searching mechanismm provides non-minimal paths (i.e., North and South) for candidate channels instead of interrupting the packet transmission. The situation is called packet detour and is similar to the search behavior of ants. Regards to the deadlock issue causing from the using of fully-minimal paths and packet detour, we also adopt the schemes in ACO-based Deadlock-Aware Routing (ACO- the occurrence of DAR) [10], which greatly suppresses deadlock while the area overhead is minor ACO-based fault-aware path selecting mechanism With the pheromone table of ACO, the path selecting mechanism chooses the better output channel from candidate output channels providedd from the path searching mechanism. The pheromone table is constructed by ant packets. The ant packets collect the network information in the routing process and update the table by state transition rule, as shown in (2). The normalized pheromone Ph (j,d) can be regarded as the probability of selecting channel index j (North, East, South, and West) for the direction of the packet transmission to destination index d. L j is the proportional to the inverse of the length of the input buffer at channel j; N k is the number of channels of current router k; and α is the weighting coefficient for the current and the historical information of the network, which ranges from zero to one. β is the fault penalty factor. (2) Figure 4. Illustrations of path searching. L j Ph'( j, Ri ) = [(1 α ) Ph( j, R i ) + α ] β j N 1 To select the better output channel in the faulty network, the Fault information, FRI, is taken into consideration for reducing the probability of selecting path to the faulty region. Since the fault may cause its nearby region to be congested, the output channel with higher value of FRI represents a limitation on the path diversity. Therefore, the fault penalty β is introduced to state transition rule when making the selection decision, but does not alter the pheromone table. According to (3), the value of β is decided by the value of FRI, and it is implemented with k 344

4 latency equals to twice of the zero-load latency, as our evaluation metric. 4.2 Performance Analysis of ACO-FAR Figure 5. Flow chart of ACO-based fault-aware routing. exponential decay. This is hardware-friendly. Note that α is constant, so the overhead of state transition rule implementation can be implemented by using a constant multiplier or even a barrier shifter. Furthermore, by making use of the existing hardware of Regional ACO-based routing [11], which reduced about 90% cost on the pheromone table, thus, the area overhead is also minor while only adding a penalty factor. FRI β = 2 (3) In summary, the flow chart is showed in Fig. 5, and the routing process is activated when a head flit arrive in input buffers. The process can finish in one cycle. Firstly, the router receives the FRI, transmitting from the adjacent nodes. Second, the path searching mechanism uses the information to determine whether the packet would be blocked by the faulty node or not. If there are no ways to route, the non-minimal paths are added. In the end, those candidate channels would be selected from the path selecting mechanism. The first simulation is the performance comparison with Modified X-First [4], FADyAD [5], Gradient [6], and ACO- FAR, as shown in Fig. 6. These routing algorithms are the turn model based fault-tolerant routing that provides lower path diversity. The traffic pattern is uniform, and each simulation has a different number of faulty routers. Note that Modified X-First routing can only handle single fault, so it is excluded from the simulation of multiple faulty nodes. In Fig. 6(a), there is the faulty router on the center of the mesh network, and the improvement from ACO-FAR to other related works are 33.3%-77.7% in saturation throughput, which conforms to previous discussion. In Fig. 6(b), there is a 2-faulty nodes, and the improvement are 41.7%-54.5%. In Fig. 6(c), 4 faulty nodes happened around the center region of the mesh, and the improvement are 40.0%-55.5%. The second simulation is the performance comparison in MMS traffic, which is the realistic traffic of the multimedia system including H.263 video encoder, an H.263 video decoder, an MP3 audio encoder, and an MP3 audio decoder. The result is also shown in Fig. 7 that the improvement of saturation throughput are 12.5%-28.6%. 4. PERFORMANCE EVALUATIONS 4.1 Simulation Environment and Setup The experiments are evaluated by SystemC NoC simulator Noxim [12]. The network topology is 8 8 mesh. While the packet length is 8 flits, 4 input buffers with the depth of 4 flits in a router. For the traffic pattern, we use the uniform traffic, and multimedia system (MMS) traffic [13]. In uniform traffic, each packet is randomly sent to each destination with the equal probability. In MMS traffic, we map and schedule 40 video/audio tasks on 25 IPs in 5 5 mesh as the realistic traffic. The simulation time is 20,000 cycles and the first 10,000 cycles is the warm-up time for measuring the performance of steady network. The performance index is the average latency under different packet injection rate. Moreover, we also adopt the saturation throughput [14], which is the throughput where the average 345

5 In Table 1, Modified X-First has the highest unreachable packet ratio and can only toleran nt a single faulty router. Gradient has relatively lower unreaachable packet ratio than FADyAD because of considering the relation among the de for preventing packet destination node and the faulty nod blockage. The restrictions of these algorithms still limit the path diversity and thus weaken theeir fault-tolerance ability. In contrast, ACO-FAR has much beetter ability to deliver the packets in the faulty network. Its paath searching mechanism provides higher path diversity to tolerant the fault and the path selecting mechanism with fault-awareness avoids n of the faulty node. routing toward the congested region The other simulation for evalu uating the fault-tolerance ability is the statistical traffic loaad distribution. It is the result of sending the same numb ber of packets with the packet injection rate at the satu uration throughput. The simulation traffic is uniform. Th he more routed packets represent the traffic load of the rou uter is heavier. The result is shown in Fig. 8, compared with Gradient G which performs relatively better than other works in n the previous simulation, the traffic load of ACO-FAR is alsso more balanced around the faulty node. TABLE 1. UNREACHABLE PACKET P RATIO. Figure 6. Performance of fault-tolerant routiing algorithms under uniform traffic with (a) 1 fault. (b) 2 ffaults. (c) 4 faults. Figure 8. The statistical traffic load at the saturation throughput of (a) ACO-FAR. (b) Grradient. Figure 7. Performance of fault-tolerant roouting algorithms under MMS traffic with 1 fault. 5. CONCLUSIONS 4.3 Evaluation of Fault-Tolerance Abilityy In this paper, we propose the ACO O-FAR that biologicallyinspired by the fault-tolerant behaavior of ants to achieve fault-tolerance for NoC, and its areaa overhead is also minor. With the proposed algorithm and d flow, our simulations show that the improvements on satturation throughput from ACO-FAR to other related fault-tollerant routing algorithms are 12.5%-77.7%. Moreover, the ACO-FAR A improves the reachable packet ratio to % and the distribution of traffic load in the faulty network. For evaluating the fault-tolerance ability, thhe index we set in the simulation is unreachable packets rattio, which is the number of unreachable packets divides byy the number of total packets injected to the network. T The unreachable packet is the packet blocked by fault nodes or traffic fully reach the congestion and thus cannot successfu destination for a long period. The simuulation traffic is uniform, and the network is an 8 8 mesh with 1, 2, and 4 faulty routers. The compared algorithms aree the same works. 346

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