The FireBox project aims to develop a system architecture for third-generation Warehouse-Scale Computers (WSCs). Firebox scales up to a ~1 MegaWatt WSC containing up to 10,000 compute nodes and up to an Exabyte (2^60 Bytes) of non-volatile memory connected via a low-latency, high-bandwidth optical switch. The FireBox project will produce custom datacenter SoCs, distributed simulation tools for warehouse-scale machines, and systems software for FireBox-style disaggregated datacenters.


The complete FireBox design contains petabytes of flash storage, large quantities of bulk DRAM, as well as high-bandwidth on-package DRAM. Each FireBox node contains a custom System-on-a-Chip (SoC) with combinations of application processors, vector machines, and custom hardware accelerators. Fast SoC network interfaces reduce the software overhead of communicating between application services and high-radix network backplane switches connected by Terabit/sec optical fibers reduce the network's contribution to tail latency. The very large non-volatile store directly supports in-memory databases, and pervasive encryption ensures that data is always protected in transit and in storage. These system characteristics raise a number of novel questions in programming environments, operating systems, and hardware design.

View Krste Asanovic's talk from FAST'14 below for more details:

The following sections summarize a variety of ongoing FireBox-related projects in the BAR group.



FireSim is a cycle-accurate FPGA-Accelerated datacenter simulation platform that uses public-cloud infrastructure to prototype FireBox. See the FireSim project page and FireSim Website for more information. You can also follow project updates on Twitter.


FireChip is a planned series of tapeouts to prototype FireBox SoCs containing out-of-order RISC-V cores, accelerators, and high-performance I/O devices.

Bulk Memory Interface

The bulk remote memory in FireBox allows us to scale memory capacity while allocating it more efficiently between compute nodes. However, the latency (and possibly bandwidth) will be higher than traditional off-package DRAM. Fast on-package DRAM allows us to mitigate some of this performance gap, but the question remains: how best to exploit it?

Swap-Based Interface

One way to harness the on-package DRAM is to use it as a large cache for the bulk memory. Many operating systems already use virtual memory to treat local memory as a cache (typically for disk). Previous work at Berkeley has shown that a swap-based approach may be feasible for some workloads given current networking technologies (paper here).

Page-Fault Acceleration

Swap-based approaches are able to transparently expose remote memory to applications, but they introduce non-trivial overheads. In our experiments, a single page fault can add 1-5us of software overhead. To improve performance further, one may be tempted to implement a fully hardware-managed DRAM cache. However, large (multiple GB) hardware caches are complex to implement and don't have the same insight as the OS. For example, the OS may choose to shrink I/O caches rather than increase swap traffic, while a HW cache would dutifully cache useless pages.

To acheive the best of both worlds, we propose a hybrid OS/HW cache. In this design, hardware manages the latency-critical page fault (cache miss), while the OS handles the complex eviction logic.



Long training times for high-accuracy deep neural networks (DNNs) impede research into new DNN architectures and slow the development of high-accuracy DNNs. FireCaffe successfully scales deep neural network training across a cluster of GPUs.

FireCaffe is designed with FireBox-style warehouse-scale computing in mind. First, we select network hardware that achieves high bandwidth between GPU servers. Second, we consider a number of communication algorithms, and we find that reduction trees are more efficient and scalable than the traditional parameter server approach. Third, we optionally increase the batch size to reduce the total quantity of communication during DNN training, and we identify hyperparameters that allow us to reproduce the small-batch accuracy while training with large batch sizes.

When training GoogLeNet and Network-in-Network on ImageNet, we achieve a 47x and 39x speedup, respectively, when training on a cluster of 128 GPUs.

Read the FireCaffe paper on arXiv.


FireBox is funded by DARPA, STARnet, CFAR, Intel, Google, HP, Huawei, LG, Nvidia, Oracle, and Samsung.