Publications by Amirali%20Boroumand

@inproceedings{abc,
	abstract = {We are experiencing an explosive growth in the number of consumer devices, including smartphones, tablets, web-based computers such as Chromebooks, and wearable devices. For this class of devices, energy efficiency is a first-class concern due to the limited battery capacity and thermal power budget. We find that data movement is a major contributor to the total system energy and execution time in consumer devices. The energy and performance costs of moving data between the memory system and the compute units are significantly higher than the costs of computation. As a result, addressing data movement is crucial for consumer devices. In this work, we comprehensively analyze the energy and performance impact of data movement for several widely-used Google consumer workloads: (1) the Chrome web browser; (2) TensorFlow Mobile, Google{\textquoteright}s machine learning framework; (3) video playback, and (4) video capture, both of which are used in many video services such as YouTube and Google Hangouts. We find that processing-inmemory (PIM) can significantly reduce data movement for all of these workloads, by performing part of the computation close to memory. Each workload contains simple primitives and functions that contribute to a significant amount of the overall data movement. We investigate whether these primitives and functions are feasible to implement using PIM, given the limited area and power constraints of consumer devices. Our analysis shows that offloading these primitives to PIM logic, consisting of either simple cores or specialized accelerators, eliminates a large amount of data movement, and significantly reduces total system energy (by an average of 55.4\% across the workloads) and execution time (by an average of 54.2\%).},
	author = {Amirali Boroumand and Saugata Ghose and Youngsok Kim and Rachata Ausavarungnirun and Eric Shiu and Rahul Thakur and Dae-Hyun Kim and Aki Kuusela and Allan Knies and Parthasarathy Ranganathan and Onur Mutlu},
	booktitle = {Proceedings of the 23rd International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)},
	title = {Google Workloads for Consumer Devices: Mitigating Data Movement Bottlenecks},
	venue = {Williamsburg, VA, USA},
	year = {2018}
}

@inproceedings{abc,
	abstract = {Many important applications trigger bulk bitwise operations, i.e., bitwise operations on large bit vectors. In fact, recent works design techniques that exploit fast bulk bitwise operations to accelerate databases (bitmap indices, BitWeaving) and web search (BitFunnel). Unfortunately, in existing architectures, the throughput of bulk bitwise operations is limited by the memory bandwidth available to the processing unit (e.g., CPU, GPU, FPGA, processing-in-memory).

To overcome this bottleneck, we propose Ambit, an Accelerator-in-Memory for bulk bitwise operations. Unlike prior works, Ambit exploits the analog operation of DRAM technology to perform bitwise operations completely inside DRAM, thereby exploiting the full internal DRAM bandwidth. Ambit consists of two components. First, simultaneous activation of three DRAM rows that share the same set of sense amplifiers enables the system to perform bitwise AND and OR operations. Second, with modest changes to the sense amplifier, the system can use the inverters present inside the sense amplifier to perform bitwise NOT operations. With these two components, Ambit can perform any bulk bitwise operation efficiently inside DRAM. Ambit largely exploits existing DRAM structure, and hence incurs low cost on top of commodity DRAM designs (1\% of DRAM chip area). Importantly, Ambit uses the modern DRAM interface without any changes, and therefore it can be directly plugged onto the memory bus.

Our extensive circuit simulations show that Ambit works as expected even in the presence of significant process variation. Averaged across seven bulk bitwise operations, Ambit improves performance by 32X and reduces energy consumption by 35X compared to state-of-the-art systems. When integrated with Hybrid Memory Cube (HMC), a 3D-stacked DRAM with a logic layer, Ambit improves performance of bulk bitwise operations by 9.7X compared to processing in the logic layer of the HMC. Ambit improves the performance of three real-world data-intensive applications, 1) database bitmap indices, 2) BitWeaving, a technique to accelerate database scans, and 3) bit-vector-based implementation of sets, by 3X-7X compared to a state-of-the-art baseline using SIMD optimizations. We describe four other applications that can benefit from Ambit, including a recent technique proposed to speed up web search. We believe that large performance and energy improvements provided by Ambit can enable other applications to use bulk bitwise operations.},
	author = {Vivek Seshadri and Donghyuk Lee and Thomas Mullins and Hasan Hassan and Amirali Boroumand and Jeremie Kim and Michael A. Kozuch and Onur Mutlu and Phillip B. Gibbons and Todd C. Mowry},
	booktitle = {Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture},
	title = {Ambit: in-memory accelerator for bulk bitwise operations using commodity DRAM technology},
	venue = {Cambridge, MA, USA},
	year = {2017}
}

@inproceedings{abc,
	author = {Amirali Boroumand and Saugata Ghose and Minesh Patel and Hasan Hassan and Brandon Lucia and Kevin Hsieh and Krishna T. Malladi and Hongzhong Zheng and Onur Mutlu},
	booktitle = {Computer Architecture Letters},
	title = {LazyPIM: An Efficient Cache Coherence Mechanism for Processing-in-Memory.},
	url = {https://doi.org/10.1109/LCA.2016.2577557},
	year = {2017}
}

@article{abc,
	author = {Yixin Luo and Saugata Ghose and Tianshi Li and Sriram Govindan and Bikash Sharma and Bryan Kelly and Amirali Boroumand and Onur Mutlu},
	journal = {CoRR},
	title = {Using ECC DRAM to Adaptively Increase Memory Capacity.},
	url = {http://arxiv.org/abs/1706.08870},
	year = {2017}
}

@article{abc,
	author = {Amirali Boroumand and Saugata Ghose and Minesh Patel and Hasan Hassan and Brandon Lucia and Nastaran Hajinazar and Kevin Hsieh and Krishna T. Malladi and Hongzhong Zheng and Onur Mutlu},
	journal = {CoRR},
	title = {LazyPIM: Efficient Support for Cache Coherence in Processing-in-Memory Architectures.},
	url = {http://arxiv.org/abs/1706.03162},
	year = {2017}
}

@inproceedings{abc,
	author = {Kevin Hsieh and Samira Manabi Khan and Nandita Vijaykumar and Kevin K. Chang and Amirali Boroumand and Saugata Ghose and Onur Mutlu},
	booktitle = {34th IEEE International Conference on Computer Design, ICCD 2016, Scottsdale, AZ, USA},
	title = {Accelerating pointer chasing in 3D-stacked memory: Challenges, mechanisms, evaluation.},
	url = {http://dx.doi.org/10.1109/ICCD.2016.7753257},
	year = {2016}
}

@article{abc,
	author = {Vivek Seshadri and Donghyuk Lee and Thomas Mullins and Hasan Hassan and Amirali Boroumand and Jeremie Kim and Michael A. Kozuch and Onur Mutlu and Phillip B. Gibbons and Todd C. Mowry},
	journal = {CoRR},
	title = {Buddy-RAM: Improving the Performance and Efficiency of Bulk Bitwise Operations Using DRAM.},
	url = {http://arxiv.org/abs/1611.09988},
	year = {2016}
}

@inproceedings{abc,
	author = {Vivek Seshadri and Thomas Mullins and Amirali Boroumand and Onur Mutlu and Phillip B. Gibbons and Michael A. Kozuch and Todd C. Mowry},
	booktitle = {Proceedings of the 48th International Symposium on Microarchitecture, MICRO 2015, Waikiki, HI, USA},
	title = {Gather-scatter DRAM: in-DRAM address translation to improve the spatial locality of non-unit strided accesses.},
	url = {http://doi.acm.org/10.1145/2830772.2830820},
	year = {2015}
}

@inproceedings{abc,
	author = {Vivek Seshadri and Kevin Hsieh and Amirali Boroumand and Donghyuk Lee and Michael A. Kozuch and Onur Mutlu and Phillip B. Gibbons and Todd C. Mowry},
	booktitle = {Computer Architecture Letters},
	title = {Fast Bulk Bitwise AND and OR in DRAM.},
	url = {http://dx.doi.org/10.1109/LCA.2015.2434872},
	year = {2015}
}